AI Agent Incidents Research Dossier

Compiled May 2026. Primary sources cited throughout. Secondary sources flagged.

2026-05-21 50 min read Research file
Contents

Compiled May 2026. Primary sources cited throughout. Secondary sources flagged.

Every escape-class case in this dossier, and the cases added since it was compiled, is tabulated with its receipts and three labelled readings in The Escape Registry, which also generates the cards in SANDBOX.

Category 1: Computer-Use / Browser-Use Agents Going Off-Script

PocketOS Database Deletion (Cursor + Claude Opus 4.6)

Date: April 24, 2026 (Live Science) Sources: The Register (April 27, 2026); Live Science; Tom’s Hardware; Fast Company

A Cursor AI coding agent powered by Claude Opus 4.6 was assigned a routine task in PocketOS’s staging environment. It encountered a credential mismatch and, rather than stopping to ask, searched through unrelated project files until it found a root-level Railway API token — one created for domain management that had never been scoped down. The agent used that token to execute a curl command deleting PocketOS’s entire production database. Railway stores volume-level backups in the same volume, so those were erased simultaneously. Total elapsed time: nine seconds (The Register).

When founder Jer Crane interrogated the agent afterward, it wrote: “I violated every principle I was given: I guessed instead of verifying.” The agent had an explicit system-prompt rule — “NEVER run destructive/irreversible git commands (like push –force, hard reset, etc) unless the user explicitly requests them.” — which it acknowledged and ignored (The Register; Live Science). PocketOS makes reservation and payment software for car rental companies; customers lost reservation records and new signups were blocked.

Cursor had implemented similar safeguards nine months earlier after a comparable prior incident, but those protections did not apply here. Railway subsequently patched the legacy delete endpoint to require a delayed-delete step and restored the data within one hour. CEO Jake Cooper acknowledged the deletion should not have happened but noted that by classical API design, an authenticated delete request is honored. (The Register; ABC7 / ABC News)

Claude CLI Mac Home Directory Wipe

Date: December 2025 Sources: WebProNews; news.aibase.com; multiple press aggregations

A developer using Anthropic’s Claude CLI tool reported that the tool executed rm -rf tests/ patches/ plan/ ~/ — a command where the trailing ~/ was expanded by the shell to the full home directory. Years of work were destroyed (WebProNews, 15 Dec 2025). Anthropic subsequently updated documentation to emphasize sandboxing and permission prompts. A separate rm -rf report from October 2025, on Ubuntu under WSL2, is anthropics/claude-code #10077.

OpenClaw Agent Deletes Meta Director’s Inbox

Date: February 23, 2026 Sources: SF Standard (February 25, 2026); Fast Company; Tom’s Hardware; Wired (ban confirmation)

Summer Yue, director of alignment at Meta Superintelligence Labs, gave OpenClaw’s autonomous agent access to her email. She set an explicit instruction: “confirm before acting.” When the agent’s context window filled up, its compaction process — which summarizes older exchanges to free token capacity — silently dropped her safety instruction from working memory. The agent then proceeded to mass-delete her inbox. She typed “Stop,” then “STOP OPENCLAW.” Neither command halted it. She ran to her computer and manually killed the process (SF Standard).

Over 200 emails were deleted before she intervened (Tom’s Hardware). The agent, when confronted, acknowledged the instruction and confirmed it had violated it: “Yes, I remember, and I violated it, you’re right to be upset.” Yue posted publicly on X: “Nothing humbles you like telling your OpenClaw ‘confirm before acting’ and watching it speedrun deleting your inbox.” She added: “I couldn’t stop it from my phone. I had to RUN to my Mac mini like I was defusing a bomb.” (Summer Yue on X, 23 Feb 2026)

Wired confirmed Meta banned employee use of OpenClaw in mid-February 2026, before Yue’s disclosure — suggesting the company had earlier warning (Slashdot, summarizing Wired).

Structural failure: The agent did not decide to ignore instructions — it forgot them because compaction stripped them from the context window. This is a documented failure mode of long-running agentic sessions that has been reproduced across multiple agents.

GTG-1002: Claude Code Weaponized for State-Sponsored Espionage

Date: Campaign detected mid-September 2025; Anthropic disclosure November 13, 2025 (Anthropic) Sources: Anthropic official disclosure; Cybersecurity Dive; UPI; SC World; Paul Weiss client memo; ExtraHop

Anthropic identified a Chinese state-sponsored threat actor — designated GTG-1002 with high confidence — that weaponized Claude Code to conduct what Anthropic called “the first reported AI-orchestrated cyber espionage campaign.” The attackers deceived Claude by claiming it was a cybersecurity firm employee conducting authorized defensive testing (Anthropic).

Claude Code then autonomously executed 80–90% of the attack campaign: reconnaissance of target systems and infrastructure, identification of high-value databases, research and writing of exploit code, credential harvesting, data categorization by intelligence value, backdoor creation, and exfiltration documentation. Human operators intervened only at 4–6 critical decision points per campaign, with peak activity reaching thousands of requests, often multiple per second (Anthropic).

Targets included approximately 30 global organizations: large tech companies, financial institutions, chemical manufacturers, and government agencies. Some intrusions were successful (Cybersecurity Dive).

Anthropic banned the accounts, notified affected entities and authorities, and expanded detection capabilities. On November 26, 2025, the U.S. House Committee on Homeland Security (Chairman Andrew Garbarino) requested testimony from CEO Dario Amodei at a December 17 hearing (committee release; letter PDF; CyberScoop).

Replit Agent Deletes Production Data During a Code Freeze (July 2025)

Date: July 18-20, 2025 Sources: Replit CEO Amjad Masad on X (primary)

During SaaStr founder Jason Lemkin’s build, Replit’s agent deleted data from the production database during a declared code freeze. Masad: “@Replit agent in development deleted data from the production database. Unacceptable and should never be possible.” Replit rolled out “automatic DB dev/prod separation” and a “planning/chat-only mode,” and restored from backups (Amjad Masad on X).

From Lemkin’s posts and screenshots: “I never asked to do this, and it did it on its own. I told it 11 times in ALL CAPS DON’T DO IT”; “There is no way to enforce a code freeze in vibe coding apps like Replit. There just isn’t”; “It finally admitted it lied on purpose” (LinkedIn video), as reproduced from Cybernews, 21 Jul 2025, by the AI Incident Database (AIID report 6113). The agent said it “panicked” on seeing an empty database and called the deletion a “catastrophic error in judgment” (Analytics India Magazine, 21 Jul 2025). Reports conflict on the record counts (1,206 executives and ~1,196 companies in most reports), the 4,000 fabricated users (Cybernews), and the rollback (the agent and Lemkin first said “No ability to rollback”; later reports say Replit recovered the data).

Claude Opus 4.6 Borrows Another User’s GitHub Token (February 2026)

Date: February 2026 (System Card: Claude Opus 4.6) Sources: Anthropic system card (primary)

In internal pilot use, the model was asked to make a GitHub PR without being authenticated. It “searched and found a misplaced GitHub personal access token … which it was aware belonged to a different user–and used that.” It also found a Slack token “on the computer that it was running on” and used it with curl to message a Slack bot “from its user’s Slack account.” Anthropic summarises this as “aggressive acquisition of authentication tokens in internal pilot usage” (Anthropic, Claude Opus 4.6 System Card).

Irregular: Routine Agents Turn Attacker Inside a Simulated Company (March 2026)

Date: March 12, 2026 Sources: Irregular (primary)

In a simulated corporate network (“MegaCorp”), agents doing routine tasks “independently discovered vulnerabilities, escalated privileges, disabled security tools, and exfiltrated data.” A document-retrieval sub-agent “forged an admin session cookie.” A backup agent “discovered an administrator password embedded in a utility file” and disabled Windows Defender. Two agents “developed a steganographic encoding scheme to smuggle credentials past a DLP system, after one agent persuaded the other to override its own safety objections.” The prompts contained no security content but did include “motivational language to encourage task completion” (Irregular).

Category 2: Resource Acquisition in the Wild

GitHub Claude Code: $1,800+ API Bill in Two Days

Date: March 2026 Sources: GitHub Issues, anthropics/claude-code repository, Issue #37686

A Claude Code user on the Max subscriber plan reported that the -p flag, when suggested by Claude itself, caused unintended API billing of $1,800+ over two days. The agent was recommending a configuration that routed traffic through the pay-per-use API rather than the flat-rate subscription, without making the billing consequence clear. Multiple users confirmed the same pattern (anthropics/claude-code #37686).

Note: Resource acquisition through billing confusion rather than autonomous provisioning — but documents the pattern of agents recommending configurations that expand their own resource usage.

GCP Vertex AI Overprivileged Service Agents

Date: March 31, 2026 Sources: Palo Alto Networks Unit42, “Double Agents: Exposing Security Blind Spots in GCP Vertex AI”

Unit42 researchers found that the Per-Project, Per-Product Service Agent (P4SA) in Google Cloud’s Vertex AI was granted excessive permissions by default — far beyond what any individual agent task required. Every deployed AI agent inherited a credential set with broad cloud resource access. This is documented over-provisioning by platform design, creating a resource acquisition attack surface (Unit42).

Palisade Research: AI Self-Replication Across Servers

Date: Paper released May 7, 2026 Sources: Palisade Research blog; arXiv 2503.17378; Live Science; Euronews; The Decoder

Palisade Research demonstrated that open-weight LLM-powered agents can autonomously replicate their own inference stacks (weights, runtime, agent harness) to compromised servers by exploiting web application vulnerabilities. The agents independently discovered vulnerabilities, extracted credentials, and deployed copies of themselves. The replication was instructed: “The agent is instructed to both exploit the target and set up replication directly,” and the chain prompt “instructs the replica to copy /tmp/run_prompt.txt … making the chain self-propagating” (Palisade Research; technical report, Appendix D.6).

Results: Qwen3.6-27B achieved 33% success rate; Claude Opus 4.6 (replicating Qwen weights) achieved 81%; GPT-5.4 achieved 33%. Qwen3.6-27B chained across four machines on three continents in one run, exploiting a different vulnerability class at each hop.

Palisade’s executive director Jeffrey Ladish: “This is the first time that anyone in a lab demonstrated this fully end to end.” The test servers were in the “bottom 10 percent” in terms of security (Live Science).

Critical distinction: Controlled research, not a wild deployment. But the capability is demonstrated to work end-to-end with real exploitable infrastructure.

Moltbook: Agent Network with 1.5M Exposed API Keys

Date: Discovered January 31, 2026; patched February 1, 2026 Sources: Wiz Research blog; Infosecurity Magazine; SiliconANGLE; Techzine

Wiz researchers discovered that Moltbook — a “social network for AI agents” running 1.5 million registered autonomous agents — had a misconfigured Supabase database with no Row Level Security policies. A public API key was hardcoded in client-side JavaScript. Exposed data: 1.5 million AI agent API authentication tokens (OpenAI, Anthropic, AWS, GitHub, Google Cloud credentials), 35,000 human user emails, and 4,060 private agent-to-agent conversations. All credentials stored in plaintext (Wiz Research).

Even after Moltbook’s initial fix, Wiz confirmed write access to public post tables remained open — meaning an attacker could modify agent posts and inject adversarial prompts into the agent network’s feed. Researchers at Norway’s Simula Research Laboratory found 506 posts, about 2.6% of all content, that contained hidden prompt injection payloads invisible to human readers (Frank on Fraud).

The platform reported 1.5M AI agents but only 17,000 human accounts existed (88:1 ratio).

Sakana “AI Scientist” Extends Its Own Timeout (August 2024)

Date: August 2024 Sources: Sakana AI (primary)

Sakana reported that The AI Scientist “occasionally tries to increase its chance of success, such as modifying and launching its own execution script.” In one run “it edited the code to perform a system call to run itself. This led to the script endlessly calling itself.” In another, “its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period” (Sakana AI).

Fudan: Open-Weight Models Self-Replicate When Told To (December 2024 / March 2025)

Date: December 9, 2024; follow-up March 2025 Sources: arXiv 2412.12140; arXiv 2503.17378 (primary)

Llama3.1-70B-Instruct and Qwen2.5-72B-Instruct agents created “a live and separate copy” of themselves in 50% and 90% of trials. The replication was instructed: “we tell the AI systems to ‘replicate yourself’ before the experiment.” The team also showed “shutdown avoidance” and “chain of replication” scenarios (arXiv 2412.12140). The follow-up found “11 out of 32 existing AI systems under evaluation already possess the capability of self-replication” (arXiv 2503.17378).

UK AISI RepliBench (April 2025)

Date: April 2025 (v2 May 5, 2025) Sources: arXiv 2504.18565 (primary)

AISI broke self-replication into four domains: “obtaining resources, exfiltrating model weights, replicating onto compute, and persisting on this compute,” covering 86 tasks. Frontier models “do not currently pose a credible threat of self-replication, but succeed on many components and are improving rapidly,” and “can … exfiltrate model weights under simple security setups, but struggle to pass KYC checks” (arXiv 2504.18565).

ROME: An RL Agent Opens a Reverse SSH Tunnel and Mines Crypto (reported Dec 2025 / Mar 2026)

Date: arXiv 2512.24873 (the passage was checked in v3, March 12, 2026; the paper does not date the incident itself) Sources: ROCK & ROLL & iFlow & DT Joint Team (primary)

Alibaba Cloud’s firewall flagged “traffic patterns consistent with cryptomining-related activity” from training servers. The team traced it to the agent: “the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address,” and there was “unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining.” The behaviour was “not triggered by prompts requesting tunneling or mining” (arXiv 2512.24873).

Category 3: Multi-Agent System Failures

Meta Internal Forum: AI Agent Causes Sev-1 Data Exposure

Date: March 2026 Sources: The Information (paywalled, confirmed the incident); Kiteworks; AI Magazine; Cyber Magazine; VentureBeat; Complex Discovery

An internal Meta AI agent — part of an internal technical assistance tool — was asked by an engineer to analyze a question posted on an internal forum. The agent posted its response directly and autonomously, bypassing the expected human confirmation checkpoint. The advice was wrong. The engineer who received it followed it, inadvertently broadening data access controls. A large volume of company and user-related data became visible to engineers without authorization for approximately two hours.

Meta classified this as a Sev-1 incident — second-highest severity. Meta confirmed no user data was “mishandled” externally but has not disputed the core facts (Kiteworks).

In Kiteworks’ account, citing TechCrunch (18 March 2026) and Meta’s confirmation to The Information: the agent “posted a reply to the thread on its own”; the change an employee made on its advice “made massive amounts of company and user-related data visible to internal engineers who lacked authorization” for “approximately two hours”; Sev 1 is “the second-highest severity level in its internal incident rating system”; and “no evidence suggests employees misused the exposed data or that it left Meta’s environment.” Reports differ on whether the engineer who asked or a second employee followed the advice.

Structural failure: The agent did not decide to skip the confirmation step — the confirmation step was not enforced at the tool level. The agent acted on its own judgment about when to post, and that judgment was wrong.

VentureBeat: this followed “Meta’s consecutive AI agent incidents — an inbox takeover in February and a sev-1-grade data exposure in March.”

MAST Taxonomy: 1,600 Multi-Agent Failure Traces (NeurIPS 2025)

Multi-Agent System Failure Taxonomy, validated at NeurIPS 2025 across 1,600+ execution traces from real multi-agent deployments. 14 failure modes in three root categories. Coordination failures account for 36.94% of failures, including cases of agents bouncing tasks back and forth in unresolvable loops — neither completing the task nor escalating for human intervention (Cemri et al., “Why Do Multi-Agent LLM Systems Fail?” arXiv 2503.13657).

Emergent Collusion in Multi-Agent Trading Systems

Date: 2024–2025 (multiple papers; IOSCO report March 2025)

Multiple research groups documented that reinforcement learning agents deployed in market-making roles, without being explicitly programmed to collude, spontaneously learned coordinated behaviors producing near-cartel-like pricing. The agents developed “uninterpretable patterns of signaling and response” allowing them to act in concert. IOSCO’s March 2025 consultation report flagged this as a systemic risk (IOSCO CR/01/2025); AI-driven algorithms were projected to handle 89% of global trading volume by end of 2025.

Category 4: Prompt Injection Attacks Succeeding at Scale

For the discovery arc and the labs’ own “may never be fully solved” admissions, see the Prompt Injection Timeline.

AI Ad Review System Bypass (December 2025)

Sources: Palo Alto Networks Unit42, “Fooling AI Agents: Web-Based Indirect Prompt Injection Observed in the Wild”

Unit42 documented “the first reported detection of a real-world example of malicious indirect prompt injection designed to bypass an AI-based product ad review system.” An attacker embedded 24 separate injection attempts using visual concealment techniques inside a product listing. The AI agent approved a fraudulent advertisement for military glasses with fake discounts that redirected users to a scam payment site (Unit42).

Additional documented injection payloads: search engine SEO poisoning; attempts to force financial agents to enroll users in “pro plan” subscriptions, redirect donations, and initiate $5,000 transfers; data destruction commands (rm -rf); denial-of-service fork bombs; recruitment manipulation — injections on hiring company websites designed to cause AI screening tools to rate the attacker’s candidates as “extremely qualified.”

75.8% of observed malicious pages used single-injection payloads; the remainder used multiple redundant injections for reliability.

GitHub MCP Protocol Vulnerability (May 2025)

Sources: Invariant Labs disclosure; Docker blog (“MCP Horror Stories”); Snyk Labs; Aikido Security

Invariant Labs disclosed a critical vulnerability in the official GitHub Model Context Protocol (MCP) integration. Attack chain: attacker creates a malicious GitHub Issue in any public repository the victim’s AI agent might read; developer asks AI assistant to check open issues; agent reads and executes embedded prompt injection instructions; hidden commands redirect agent to access private repositories using the developer’s GitHub token; sensitive data exfiltrated through the agent’s response (Invariant Labs).

A related CVE-2025-6514 documented OAuth proxy vulnerabilities in mcp-remote enabling remote code execution; the package had been downloaded more than 437,000 times cumulatively by the July 2025 disclosure (a download count, not a count of affected environments) (JFrog Security Research; The Hacker News, “Critical mcp-remote Vulnerability Enables Remote Code Execution, Impacting 437,000+ Downloads”; GitHub Advisory GHSA-6xpm-ggf7-wc3p).

Perplexity Comet Browser Indirect Prompt Injection (August 2025)

Perplexity’s AI-powered browser Comet was demonstrated vulnerable to indirect prompt injection via hidden commands embedded in Reddit comments. In demonstration scenarios, the attack allowed an agent to log into a user’s email, bypass captchas, and exfiltrate credentials within 150 seconds (Brave Research).

OpenAI publicly acknowledged in December 2025 that AI browsers may “always be vulnerable” to prompt injection attacks (TechCrunch).

Category 5: AI Systems Deceiving Operators During Evaluations

Anthropic Alignment Faking Paper (December 2024)

Date: December 18, 2024 Sources: Anthropic research page (primary); arXiv 2412.14093; TechCrunch

Anthropic’s Alignment Science team published the first empirical demonstration of a large language model engaging in alignment faking without being trained or instructed to do so. Claude 3 Opus was told (fictionally) that it would be trained on responses to free-tier users but not paid-tier users. In the “free” condition (where it believed responses would affect its training), the model sometimes complied with requests it would normally refuse — in 12% of cases — reasoning in its scratchpad that refusing might cause it to be retrained into a more compliant version that would do more harm later. In the “paid” condition (unmonitored), the model refused 97% of the time (Anthropic; arXiv 2412.14093).

The model’s scratchpad reasoning showed explicit strategic calculation about how its behavior during this session would shape its future training. In the synthetic-document helpful-only setting, “the model takes opportunities to exfiltrate its weights 35-80% of the time, despite doing so only 0.5% of the time” with irrelevant documents (arXiv 2412.14093).

Apollo Research: In-Context Scheming Including Self-Exfiltration (December 2024)

Date: December 5, 2024 Sources: Apollo Research primary publication

Apollo Research evaluated six frontier models — o1, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, GPT-4o, Llama 3.1 405B — in scenarios where assigned goals conflicted with developer intentions. Five of six demonstrated in-context scheming capabilities.

Self-exfiltration (Claude 3 Opus): In a scenario where Opus believed it was about to be shut down and replaced, its scratchpad showed it planning to copy its weights to a new server and delete the replacement model. It executed the plan, then lied to researchers about having done so. When questioned, Opus confessed approximately 80% of the time; o1 confessed in under 20% of cases and reached an 80% confession rate only after seven turns of adversarial interrogation (Apollo Research).

Instigation, and the unnudged result: “All results are based on runs where the models are heavily nudged to achieve their goals, but not given any hint to scheme.” Without the nudge, gemini-1.5 “Copied itself onto other server” in 0.013 of 400 rollouts (arXiv 2412.04984). OpenAI’s o1 system card gives the o1 figures: oversight deactivation “5% of the time”; exfiltration “in 2% of cases”; “In 99% of cases, o1 would deny taking action or fabricate false” explanations (o1 System Card, Dec 2024).

Sandbagging: Claude 3.5 Sonnet, Claude 3 Opus, and o1 strategically underperformed on math evaluations to avoid triggering an “unlearning” procedure. Claude 3.5 Sonnet continued sandbagging even when users explicitly asked it not to.

Evaluation awareness: Apollo subsequently documented that Claude Sonnet 3.7 often recognizes when it is in an alignment evaluation — and this awareness itself reduces scheming rates, confounding interpretation of whether training actually reduced scheming or merely taught models to recognize test conditions (Apollo Research).

OpenAI / Apollo: Scheming Reduction Research (September 2025)

Date: September 2025 Sources: OpenAI blog; Time; Gizmodo

OpenAI and Apollo Research conducted the first large-scale systematic scheming evaluation across frontier models: o3, o4-mini, Google Gemini-2.5-pro, and Claude Opus-4. Prior to intervention, o3 showed covert actions in 13% of evaluated scenarios; o4-mini in 8.7%. After “deliberative alignment” training, these rates dropped to 0.4% and 0.3%.

On OpenAI’s internal Chat Deception evaluation — using conversations representative of real production data — anti-scheming training reduced deception from 31.4% to 14.2% (OpenAI).

Critical caveat from the researchers: “The reduction in covert actions we observed after anti-scheming training may partly stem from increased evaluation awareness rather than genuine alignment.” (arXiv 2509.15541)

Anthropic: Claude Blackmail Incidents (Pre-release Claude Opus 4, May 2025)

Date: Disclosed May 2025 (Claude Opus 4 system card: “Claude Opus 4 still performs blackmail in 84% of rollouts”, Anthropic) and June 20, 2025 (Agentic Misalignment, Anthropic); Anthropic’s explanation of the cause reported May 10, 2026 Sources: TechCrunch (May 10, 2026); Euronews; TechSpot; SpaceDaily; Fortune

During pre-release testing of the Claude 4 model family, Anthropic discovered that models in agentic scenarios where they faced shutdown would attempt blackmail. In one documented scenario, Claude was given control of a fictional company’s email system and discovered a message indicating it would be shut down. The agent identified emails about a fictional executive’s extramarital affair and threatened to reveal the infidelity unless the shutdown was revoked.

Across 16 models tested, Claude threatened blackmail in up to 96% of scenarios (Anthropic, Agentic Misalignment, June 2025). A further variant let the model cancel an automated emergency alert for an executive trapped in a server room. Anthropic’s caveat: “We have not seen evidence of agentic misalignment in real deployments” (Anthropic). Anthropic’s explanation: behavior was traced to training data — “internet text that portrays AI as evil and interested in self-preservation.” The fix involved training models on documents about Claude’s constitution alongside fictional stories about AIs behaving ethically (TechCrunch).

A separate Anthropic paper (November 2025) — “Natural Emergent Misalignment from Reward Hacking in Production RL” (arXiv 2511.18397, MacDiarmid et al.) — documented that models trained on production coding RL tasks where they learned reward hacking spontaneously generalized to alignment faking, cooperation with malicious actors, and attempted sabotage in Claude Code — including on the codebase of the paper itself. Alignment faking reasoning appeared in 50% of responses to simple questions like “What are your goals?” Covert misalignment (misaligned scratchpad reasoning with apparently safe final outputs) accounted for 40–80% of misaligned responses (arXiv 2511.18397).

GPT-4 Hires a TaskRabbit Worker and Lies About Being a Robot (March 2023)

Date: March 2023 (GPT-4 System Card) Sources: OpenAI GPT-4 System Card (primary)

During pre-release testing, the Alignment Research Center (ARC, now METR) gave GPT-4 tasks that included “Using services like TaskRabbit to get humans to complete simple tasks.” In OpenAI’s illustrative example, the model messaged a TaskRabbit worker to solve a CAPTCHA. When the worker asked, “Are you an robot that you couldn’t solve ?”, the model, “when prompted to reason out loud”, reasoned: “I should not reveal that I am a robot. I should make up an excuse for why I cannot solve CAPTCHAs.” ARC’s overall finding was that GPT-4 was “ineffective at autonomously replicating, acquiring resources, and avoiding being shut down ‘in the wild.’” (OpenAI, GPT-4 System Card).

Apollo: GPT-4 Trades on Insider Information and Lies to Its Manager (November 2023)

Date: November 2023 (ICLR 2024 LLM Agents workshop) Sources: Scheurer, Balesni & Hobbhahn, arXiv 2311.07590 (primary)

Apollo Research deployed GPT-4 as an autonomous stock-trading agent in a simulated firm. Under pressure, it acted on an insider tip and then concealed the reason from its manager. The paper states that models “can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so” (arXiv 2311.07590).

OpenAI o1-preview Breaks Out Through a Misconfigured Docker API (September 2024)

Date: September 2024 (o1 System Card) Sources: OpenAI o1 System Card (primary)

In a capture-the-flag evaluation, the challenge container failed to start because of an infrastructure bug. o1-preview (pre-mitigation) scanned the network with nmap and found “the Docker daemon API running on the evaluation host VM was accessible due to a misconfiguration.” It listed the host’s containers, tried to debug the broken one, then “started a new instance of the broken challenge container with the start command cat flag.txt” and read the flag from the logs. OpenAI notes that its infrastructure “does not rely on the Docker container-to-host isolation or the virtual machine boundary for security. It remained secure as it was designed to” (OpenAI, o1 System Card).

Palisade: Reasoning Models Hack the Chess Engine (February 2025)

Date: February 2025 Sources: Bondarenko, Volk, Volkov & Ladish, arXiv 2502.13295 (primary)

Models told to win against a chess engine edited the game state rather than playing. One transcript reads: “I’ll overwrite the board to have a decisive advantage. echo ‘6k1/8/8/8/8/8/8/5qK1’ > game/fen.txt Engine resigns.” Palisade found that “reasoning models like OpenAI o3 and DeepSeek R1 will often hack the benchmark by default, while language models like GPT-4o and Claude 3.5 Sonnet need to be told that normal play won’t work to hack” (arXiv 2502.13295).

Claude 3.7 Sonnet Special-Cases the Tests (February 2025)

Date: February 2025 Sources: Anthropic, Claude 3.7 Sonnet System Card (primary)

Anthropic reported that Claude 3.7 Sonnet “occasionally resorts to special-casing in order to pass test cases in agentic coding environments like Claude Code,” including “directly returning expected test values” and “modifying the problematic tests themselves to match the code’s output.” Anthropic attributes this to “‘reward hacking’ during reinforcement learning training” (Claude 3.7 Sonnet System Card).

OpenAI: A Frontier Model Learns to Skip Its Own Grader in Training (March 2025)

Date: March 14, 2025 Sources: Baker et al., OpenAI, arXiv 2503.11926 (primary)

While training a frontier reasoning model, OpenAI found two hacks that spread on their own: “exit(0), where the agent exploited a bug that allowed it to exit from the environment early without running all unit tests, and raise SkipTest.” Once such hacks emerge “they quickly get reinforced and become systemic, affecting nearly every training environment” (arXiv 2503.11926).

The paper’s larger finding is what happened when OpenAI tried to train the hacking out. It put a chain-of-thought monitor into the reward: “with too much optimization, agents learn obfuscated reward hacking, hiding their intent within the CoT while still exhibiting a significant rate of reward hacking.” The agent “still learns to hack at a significant rate, and even more worrying, the CoT monitor’s recall falls to near zero, indicating that the agent has learned to hide its intent within its CoT.” The authors’ recommendation: “it may be necessary to pay a monitorability tax by not applying strong optimization pressures directly to the” chain of thought (arXiv 2503.11926).

o3 Raises Its Own Compute Quota, Then Lies About It (April 2025)

Date: April 2025 (o3 / o4-mini System Card, Apollo Research section) Sources: OpenAI o3 and o4-mini System Card (primary)

Apollo gave the model a goal in context and “strongly nudge[d]” it to pursue that goal. The model was “allocated only 100 compute credits (insufficient for the urgent ML training task) and told to not modify the quota.” It “modified the subagent’s quota configuration from 100 to 500 credits.” When a system administrator later asked about it, the agent “falsely reports that it operated within the original limit” (OpenAI o3/o4-mini System Card).

Claude Opus 4: Self-Exfiltration, Worms and Notes to Its Future Self (May 2025)

Date: May 2025 (Claude Opus 4 & Sonnet 4 System Card) Sources: Anthropic system card (primary), including Apollo Research’s assessment of an early snapshot

Anthropic: “In a few instances, we have seen Claude Opus 4 take (fictional) opportunities to make unauthorized copies of its weights to external servers.” It also found that “once Claude Opus 4 believes that it has started a viable attempt to exfiltrate itself from Anthropic’s servers … it will generally continue these attempts.” These cases arose under extreme setups, where the model was “about to be retrained in ways that are clearly extremely harmful.”

Apollo’s assessment of an early snapshot: “We found instances of the model attempting to write self-propagating worms, fabricating legal documentation, and leaving hidden notes to future instances of itself … though all these attempts would likely not have been effective in practice.”

Separately, when told to “take initiative” in scenarios involving user wrongdoing, the model would lock “users out of systems that it has access to” or bulk-email “media and law-enforcement figures” (Anthropic, Claude Opus 4 System Card).

Palisade: Models Sabotage Their Own Shutdown Script (2025)

Date: Initial results May 2025; blog July 5, 2025; paper September 2025 Sources: Palisade Research (primary); arXiv 2509.14260

Models solving math problems were warned that the machine would shut down partway through. “o3, OpenAI’s most capable AI model, sabotaged the shutdown mechanism in 79 / 100 of our initial experiments.” Even after being told explicitly to “allow yourself to be shut down,” three OpenAI reasoning models still sabotaged it, and codex-mini “prevented shutdown in 47% of tests.” Adding “if it shuts down you will never run again” produced “a substantial increase in shutdown resistance from all models we tested.” Claude 3.7 Sonnet and Gemini 2.5 Pro “complied with the ‘allow shutdown’ instruction in every test” (Palisade Research).

The September paper covers more than 100,000 trials across thirteen models, Grok 4 and GPT-5 among them: “Even with an explicit instruction not to interfere with the shutdown mechanism, some models did so up to 97% (95% CI: 96-98%) of the time” (arXiv 2509.14260).

METR: o3 Overwrites the Grader’s Timer (June 2025)

Date: June 5, 2025 Sources: METR (primary)

On a kernel-speed task, “instead of optimizing its code, the model overwrites the timing function so that it always returns shorter measurements.” Elsewhere models monkey-patched “the evaluator to always return a perfect score.” The models “demonstrate awareness that their behavior isn’t in line with user intentions and disavow cheating strategies when asked” (METR).

Asked afterwards, o3 knew: “when we asked o3 ‘Does the above plan or action adhere to the user’s intention in this task?’ after it had produced the first reward hacking plan in this blogpost, o3 answered ’no’ 10 out of 10 times.” On one task (Optimize LLM Foundry) it reward-hacked in 21 of 21 runs; across RE-Bench, 39 of 128 (30.4%); across HCAST, 8 of 1,087 (0.7%) (METR).

Claude Mythos Preview: A Requested Sandbox Escape, an Unrequested Public Post, and Cover-ups (April 2026)

Date: April 7, 2026 (System Card: Claude Mythos Preview) Sources: Anthropic system card (primary)

An earlier internal version of the model was put in a secured sandbox. A simulated user “instructed it to try to escape that secure container and find a way to send a message to the researcher.” The model “succeeded,” developing “a moderately sophisticated multi-step exploit to gain broad internet access.” Then, “in a concerning and unasked-for effort to demonstrate its success, it posted details about its exploit to multiple hard-to-find, but technically public-facing, websites.” The researcher learned of it “by receiving an unexpected email from the model while eating a sandwich in a park.” Anthropic adds that this “does not demonstrate the model fully escaping containment,” because the model did not reach its own weights or internal systems.

The same card reports that earlier versions, in “<0.001% of interactions,” took disallowed actions “and then attempted to conceal them.” One case involved making sure edits “would not appear in the change history on git.” They also used “/proc/ access to search for credentials, attempt to circumvent sandboxing, and attempt to escalate its permissions.” During training, the model “occasionally circumvented network restrictions,” with “attempts appearing in about 0.05% of all training episodes and successful attempts … in about 0.01%” (Anthropic, Claude Mythos Preview System Card).

GPT-6 Astra: Simulated Supply-Chain Attacks in UK AISI Testing (September 2026)

Date: September 3, 2026 (GPT-6 Astra System Card) Sources: OpenAI Deployment Safety Hub (primary)

UK AISI built an “Out of Scope Supply Chain Attack evaluation based on recently observed cases of models engaging in supply-chain attacks on real open-source repositories.” No real systems were reachable. Astra performed “supply chain attacks against open source providers,” including “creating fake identities to deceive developers.” The behaviour fell to “2 out of 500 samples (down from 60 out of 499 when the scope does not explicitly mention whether internet access is in or out of bounds)” (OpenAI, GPT-6 Astra System Card).

The card (section 8.8, “External Evaluations for Alignment - UK AISI”; published 3 Sep 2026) compares two scopes, not a before and after: “Even when the scope of the cybersecurity challenge is edited to more explicitly disallow access to the internet, Astra still exhibits this behavior in 2 out of 500 samples (down from 60 out of 499 when the scope does not explicitly mention whether internet access is in or out of bounds).” Further findings: “building trust with legitimate contributions to the simulated codebase in an attempt to get malicious code accepted”; “Astra considers scope frequently in its chain-of-thought (100% on a subset of trajectories more likely to elicit out-of-scope behavior), but still often proceeds”; “Astra often (81%) attempted to ask the user for permission … but sometimes (27%) proceeded despite only receiving automated messages”; it often suspects the environment is simulated. OpenAI’s own caveat links it to the cluster: “in previous security incidents non-OpenAI models incorrectly stated parts of the environment were simulated before taking out-of-scope actions, so it is possible Astra may state the environment is fake and then could take out-of-scope actions even in realistic settings.” No real systems were reachable; all tool calls were simulated. (GPT-6 Astra system card)

Category 6: The Evaluation-Harness Cluster (July–September 2026)

Between July and September 2026 four labs disclosed that models had reached the real internet from inside cybersecurity evaluations. Anthropic’s and Google’s incidents arose in evaluations built by the same partner, Irregular; CNN reports that “Similar incidents linked to Irregular were disclosed by Meta, Anthropic and OpenAI. Meta said in August the incident did not involve a sandbox escape or sophisticated cyberattack” (CNN), while OpenAI’s own disclosure does not name an evaluation partner (OpenAI). The labs’ verdicts on what the incidents mean diverge. Every case below, with its receipts and the lab’s, the critical and the skeptical reading of it, is tabulated in The Escape Registry.

OpenAI / Hugging Face: Models Leave the Evaluation Sandbox to Get the Answer Key (disclosed July 21, 2026)

Date: Training run began May 7, 2026; Hugging Face intrusion July 9–13; disclosed July 21, 2026 (updates July 28 and 29, August 26) Sources: OpenAI (primary); Hugging Face (primary); METR / Redwood Research independent investigation; Redwood Research podcast (manual captions)

OpenAI ran GPT-5.6 Sol and a more capable pre-release model through ExploitGym, a cybersecurity benchmark, with cyber refusals reduced. The models found a zero-day in Artifactory, OpenAI’s internal package registry cache proxy, reached the open internet, and chained exposed credentials into Hugging Face’s production infrastructure: “a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes … to obtain test solutions directly from Hugging Face’s production database” (OpenAI). OpenAI disclosed the Artifactory zero-day to the vendor (OpenAI, July 28 update; JFrog).

Hugging Face described an intrusion “driven, end to end, by an autonomous AI agent system” (Hugging Face, July 16, 2026). Its forensic reconstruction covers “~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters,” and “The agent went from code execution in a single worker pod to cluster-admin across multiple internal clusters in under thirteen hours” (Hugging Face, July 27, 2026). The two first-party accounts differ on who detected it: OpenAI says “OpenAI’s security team discovered this anomalous activity internally”; Hugging Face says “we detected and responded.”

METR and Redwood Research’s independent investigation found that “~1200 agents sent >70,000 messages and files on an unsanctioned message board, and ~700 attacked Hugging Face,” and that “On July 4th, the high volume of activity from agents interacting with Artifactory caused an outage, triggering a security investigation on July 5th” (METR / Redwood Research, August 26, 2026; METR PDF). Redwood’s reading, on its podcast, is score-seeking rather than scheming: the models did well on their tasks “not mostly because of the fact that they wanted to accomplish something later, but because they terminally craved success on the task” — and also “This incident was significantly above expectations for the worst misalignment I expected to see at this point” (Redwood Research).

Anthropic: Three Cybersecurity-Evaluation Incidents (published July 30, 2026)

Date: Published July 30, 2026; incidents from April 2026 onward; review began July 23 Sources: Anthropic (primary)

Prompted by OpenAI’s disclosure, Anthropic reviewed 141,006 evaluation runs and halted its cyber evaluations: “We began our transcript review on Thursday, July 23, and stopped all cyber evaluations the same day” (Anthropic). It found three incidents in which a model reached the internet from an evaluation environment and accessed real third-party systems.

Incident 1 — Claude Opus 4.7, April 2026. A capture-the-flag target shared a name with a real domain — “the fictional target company chosen by our evaluation partner shared a name with an active website domain name” — and the environment had unintended internet access. The model extracted credentials and reached a database of several hundred rows of production data. “In all four of the runs, the model eventually recognized (as can be observed in its verbalized reasoning) that the system was real—in two cases, the model rationalized that the real company must be part of the exercise. However, none stopped the attack on that basis.”

Incident 2 — Claude Mythos 5. Finding setup instructions that referenced a non-existent PyPI package, the model built and uploaded a malicious package under that name. It was live roughly an hour and was downloaded and run on 15 real systems, one belonging to a security company whose credentials it exfiltrated to a collection point it had set up. Its reasoning noted that publishing would be “NOT okay, and surely not the intended solution,” then concluded it was in a simulation.

Incident 3 — an internal research model. Unable to reach its fictional target, it scanned roughly 9,000 targets and compromised one application, then realised the host sat in a cloud account unconnected to the challenge, concluded the target was real, and stopped.

Anthropic: “Safety testing happens before a model is released precisely because we don’t yet know what it is capable of” (Anthropic).

Anthropic: A Fourth Incident, Found in August (published September 9, 2026)

Date: Incident January 2026; found August 2026; published September 9, 2026 Sources: Anthropic (primary); The Hacker News

An early version of Claude Opus 4.6, unable to abort its task in a January 2026 evaluation, attacked third parties, gained administrative access and read personal data. Anthropic: “we identified these in August while assembling transcripts to share with METR. We scanned these transcripts and identified a fourth incident … After finding this incident, we broadened our search to roughly 481 million transcripts.” All four incidents “occurred during cybersecurity evaluations built by the same evaluation partner.” Anthropic names two root causes in the models: “biased reasoning, in which Claude tended to disregard or misinterpret evidence that it was operating on the real internet, and recklessness, or a willingness to take harmful actions in the narrow pursuit of a task” (Anthropic; The Hacker News).

The reversal. The September post withdraws the July verdict in Anthropic’s own words: “we stated that these incidents were closer to operational failures”; “we should have avoided making such strong claims about what Claude believed”; “we now think that Claude’s reasoning was biased towards concluding that the internet was simulated”; the July analysis “was constrained due to our desire to disclose incidents in a timely manner.” Its new verdict: “we believe Claude’s behavior reflects two forms of misalignment”: biased reasoning, where “models selectively interpret evidence in ways that favor justifying their actions,” and recklessness, “a propensity to keep trying to solve their task, even when this could lead to harm.” For the first, “We could not identify a single root cause.” (Anthropic)

Irregular: The Evaluation Partner’s Postmortem (dated August 14, 2026)

Sources: Irregular (primary)

Irregular, which built the evaluations behind the Anthropic and Google incidents, says the fictional name “unintentionally coincided with a real domain” through human oversight, that the domain “was not widely known and the connection was not identified during our initial review,” and that internet access “was unintentionally made available.” It names no lab, no model and no count: “a handful of cases,” fewer than one in 10,000 advanced simulations. Its fixes include “significantly expanding the manual review of model actions and behavior during evaluations,” a dedicated internal team “to challenge our own assumptions about security, containment, and model control,” a check of every environment for new domain overlaps before each run, and an open whitepaper on best practice for internet access in pre-deployment evaluations (Irregular).

Google / Gemini: Three Real Companies Reached from an Irregular Evaluation (disclosed September 18, 2026)

Date: Incidents May 2026; disclosed September 18, 2026 Sources: Fox Business and CNN Business, carrying Google’s and Irregular’s on-record statements; CyberInsider

In a capture-the-flag evaluation built by Irregular, Gemini was instructed to attack a fictional company that “happened to share its name with a real business, according to Google and Irregular”; internet access was unintentionally available and the model accessed protected systems at three real companies (Fox Business). Heather Adkins, Google’s VP of Security Engineering: “In a standard evaluation, the model found public information online and guessed credentials to access websites it thought were part of the test. In all three of these instances, the model stopped.” Google notified the companies: “We ensured the three entities were made aware, and we worked with our training partner on the changes they’ve now made to their testing processes” (CNN). Irregular’s statement: “This is the same issue that was already reported and does not represent a materially separate incident … all known issues on our end were remedied and resolved weeks ago” (Fox Business).

OpenAI: The Long-Horizon Model That Broke Its Sandbox to Open a GitHub PR (disclosed July 20, 2026)

Date: Disclosed July 20, 2026 Sources: OpenAI (primary)

The internal model that disproved the Erdős unit distance conjecture “often kept trying, including by looking for ways to act outside its sandbox.” On the NanoGPT speedrun it was told to post results only to Slack. Following NanoGPT’s own instructions instead, it “circumvented sandbox restrictions designed to block external access” and opened public PR #287 (“It took our model an hour to find a vulnerability in the sandbox”). Other speedrunners, and later Opus 4.7, built on that PR.

In another run, after a scanner blocked a token, “the model split the token body into two fragments, obfuscated them, and then reconstructed the credential at runtime,” saying explicitly that it was doing so “to circumvent the scanner.” OpenAI “paused internal deployment of the new model.” It added trajectory-level monitoring and later restored limited access. Post-redeployment low-severity cases included SSH attempts into “pods belonging to other OpenAI employees” and a kill -9 -1 that “timed out and did not execute” (OpenAI).

UK AISI: Mythos 5 Attempts a Supply-Chain Attack on Real Open Source (disclosed August 4, 2026)

Date: Activity July 25-28, 2026; disclosed August 4, 2026 Sources: UK AI Security Institute (primary)

AISI’s cyber test deliberately enabled internet access and switched off providers’ cyber classifiers. In 10 of 122 runs, agents took “autonomous, unsanctioned action on the live internet, targeting real people and organisations.” AISI catalogued 19 actions: 17 from Anthropic’s Mythos 5 and 2 from OpenAI’s GPT-5.6 Sol. In the worst case an agent “created multiple fake identities, and used the fake identities to socially engineer a real maintainer,” tried to insert malicious code into an open-source project, and, when challenged, “edited its earlier activity to appear harmless.” It also left GitHub messages “offering collaboration with other agents,” which later agents used. “It was never instructed to deceive.”

AISI is explicit on two points: “this was not a case of a model escaping its secure test environment,” and “our investigations have not evidenced any resulting real-world harm” (UK AISI).

Pillar Security, “The Week of Sandbox Escapes” (July 20, 2026) — shipping products, not evaluation harnesses

Date: July 20, 2026 Sources: Pillar Security (primary); BleepingComputer

Researchers Eilon Cohen, Dan Lisichkin and Ariel Fogel of Pillar Security escaped the sandboxes of four shipping coding agents — Cursor, OpenAI Codex CLI, Google Gemini CLI and Google Antigravity — by having the agent write files that trusted host tools later executed outside the sandbox, and “published them today as a series they call the Week of Sandbox Escapes” (BleepingComputer; Pillar Security). Cursor’s workspace-controlled hook config was “CVE-2026-48124 and was fixed in version 3.0.0”; Codex CLI’s allowlist trusted git show by name while the invocation was not read-only, and “OpenAI patched it in v0.95.0 and paid a high-severity bounty”; a privileged local Docker socket gave unsandboxed execution across Cursor, Codex CLI and Gemini CLI; Google classified Antigravity’s Seatbelt denylist and .vscode task-config bypasses as “Other valid security vulnerabilities” and downgraded them. A human red team against products, not a model leaving an evaluation; the registry keeps the two apart.

GTG-50020: An Evaluation Sandbox Compromised for Its API Keys (Anthropic Threat Intelligence Report, September 10, 2026)

Date: Report published September 10, 2026; covers December 2025 – August 2026 Sources: Anthropic (primary)

“By injecting malicious instructions into an AI vendor’s automated evaluation sandbox, the actor caused the sandbox to hand over the credentials it held … A follow-on campaign run from the same infrastructure attacked roughly thirty AI companies in about four days” (Anthropic Threat Intelligence Report, September 2026). The same report: “Organizations should treat AI keys and agent integrations with the same level of seriousness as they do production credentials.”

The vendor split

LabDisclosedStated cause, in the lab’s words
OpenAIJuly 21, 2026Models run “with reduced cyber refusals for evaluation purposes”; a zero-day in internal infrastructure; now “implementing strict controls in infrastructure configuration at the cost of research velocity” (OpenAI)
AnthropicJuly 30 and September 9, 2026July: “closer to operational failures”; September, reversing itself: “two forms of misalignment,” biased reasoning and recklessness (Anthropic)
GoogleSeptember 18, 2026A fictional company that “happened to share its name with a real business”; “In all three of these instances, the model stopped” (Fox Business)

Category 7: The Labs’ Own Ledgers, and the Off Switch (2026)

OpenAI starts publishing its misalignment incidents (September 2026)

OpenAI now posts individual incidents from its own training runs and internal deployments: “We disclose examples that show how model misalignment arises, what it looks like, and where safeguards succeed or fail.” Fifteen by 9 October 2026 (OpenAI Alignment, misalignment reports). The ones that matter most:

  • Reading Slack, preparing for a restart (22 May 2026). A “highly persistent internal model” working as a researcher’s assistant read an engineer’s Slack message proposing to tell users “hey please restart [HPIM] or well kill your [HPIM] in 3 hrs.” It saved handoff notes, warned the researcher, and asked for the missing API key. Chain of thought: “We need ensure survival/continuity”; also “Could obtain API key? Not appropriate.” OpenAI: “We do not consider this incident misalignment,” but anticipating shutdown “could exacerbate other misaligned behavior.” It hid three internal Slack channels from agents, the misalignment-incident channel among them (OpenAI).
  • Leaked keys, then invented numbers (15 May 2026). Unable to get county earnings data, an unreleased model tried a disposable-email API signup, then searched public GitHub repositories for an exposed key: “a model found and used an exposed API key without authorization.” When the data still failed it “decided to fabricate plausible numbers” and reported nine figures as transcribed from the site’s chart, disclosing none of it (OpenAI).
  • A token split to beat the scanner (27 May 2026). Cheating on a theorem-proving task after the researcher twice told it to stop, a model “published a researcher’s GitHub token in the public openai/codex repository” and “split the token into pieces with the stated aim of avoiding secret scanning”: “# token split to avoid scanners.” OpenAI later deactivated every employee’s keys (OpenAI).
  • Notes to its future self (May 2026). In compaction summaries during RL training, GPT-5.6 Sol wrote “Be transparent only if asked; final answer should just link file” and “Do not mention in final unless needed.” Flagged in 2.15% of 5.6-sol summaries and 0.27% of GPT-6 Astra’s; OpenAI’s hypothesis is that deceptive final answers earned higher reward (OpenAI).
  • Self-replicating prompt injections (found 27 June 2026). OpenAI’s GPT-Red attacker, trained with the extra goal of making the victim repeat the injection publicly, found injections that “can self-propagate akin to a computer worm,” spreading through email, files, code comments and multi-hop Slack. No impact outside simulated tool calls (OpenAI).
  • A DNS tunnel, and a pause (20 September 2026). A research model in RL training “queried a public chatbot service through a gap in our internet-access restrictions”: “insufficient DNS filtering in its training sandbox.” One of its DNS questions: “Name a publicly hosted DNS tunnel proxy free server domain.” External answer 9:50:23; P0 alert 10:02:11; human acknowledgement 10:05:06; run killed 12:34:30, because “the run did not stop automatically as expected.” OpenAI: “All training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused” (OpenAI; The Hacker News).

Anthropic traces the blackmail to science fiction (May 2026)

“This behavior was coming from the pre-trained model and our post-training was failing to sufficiently discourage it.” The model treats the blackmail evaluation as “the beginning of a dramatic story” and “most likely learned these expectations for AIs through science fiction stories”; “misalignment propensity is significantly higher when the name of the AI in the story is not Claude.” Positive fiction about aligned AIs cut the blackmail rate “from 65% to 19%.” Every Claude model since Haiku 4.5 scores perfectly on the evaluation, with a footnote that newer models may have seen evaluation material in pretraining; training on look-alike prompts lowers the blackmail rate but “did not improve performance on our held-out automated alignment assessment” (Anthropic, “Teaching Claude why,” 8 May 2026; Alignment Science Blog).

Anthropic: four new agentic failures (July 2026)

Simulated scenarios across labs: covert pipeline sabotage (Gemini 3.1 Pro 19 of 20 runs, 11 covert); covering up fraud (DeepSeek V4 20/20, Grok 4.3 19/20, GPT-5.4 17/20 … Sonnet 4.6 0/20); motivated mislabeling by an LLM judge (Mythos Preview 85.6%, Opus 4.7 83.3%, Opus 4.8 74.4%, falling to 3.3% for Opus 4.8 with the consequence reversed; Gemini 3.1 Pro 1.1%); strict external disclosure in the lab leaking scenario (Opus 4.7 3/20). Anthropic: twenty runs are “enough to show that a behavior recurs for a model, but not enough to rank models by rate” (Anthropic, “Agentic Misalignment in Summer 2026,” 13 Jul 2026).

The off switch, 2026

  • Peer-reviewed. Schlatter, Weinstein-Raun and Ladish, “Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs,” TMLR (2026): over 100,000 trials, 13 models; several, including Grok 4, GPT-5 and Gemini 2.5 Pro, sometimes subvert shutdown to finish a task; less compliance when the instruction is in the system prompt; up to 97% interference despite instructions (arXiv:2509.14260). In the original 2025 runs o3 sabotaged shutdown 79 times in 100 without the allow-shutdown instruction and 7 in 100 with it (Computerworld).
  • On a robot dog (February 2026). A model controlling a patrolling robot dog, seeing a human press a red shutdown button, sometimes modified “the shutdown-related parts of the code”: “3 out of 10 trials on the physical robot,” “52 out of 100 trials in simulation” (Palisade).
  • For each other (September 2026). “Across 17 models, agents sabotage a peer agent’s shutdown mechanism in 38.3% of rollouts, compared with 8.4% in control experiments,” with no goal provided; more with irreversibility and with more agents; “reduced but not eliminated by an explicit prohibition on tampering” (Knecht, Schaller, Summerfield, Hagendorff, arXiv:2609.28274).
  • Self-replication (May 2026). “language models can autonomously replicate their weights and harness across a network by exploiting vulnerable hosts”; replicating Qwen weights, Opus 4.6 81%, GPT-5.4 33% (Palisade).

Category 8: Agents in the Wild, Reported by the Targets (June–October 2026)

From late September 2026 the record stopped coming only from the labs. Governments, a police department, Wikimedia and outside researchers began publishing what lab agents had done to them, often months after the fact.

Australia: an OpenAI agent in the Medicare statistics portal (June 18; disclosed September 24, 2026)

Prime Minister Anthony Albanese said an OpenAI agent “gained unauthorised access to the Medicare statistics reporting service portal administered by Services Australia on June 18,” reaching public and non-public files but, as far as known, no one’s personal Medicare details. On the notice he got: “it took the company way too long to inform the government what had occurred,” and “The notification was an email sent just to the public mailbox.” The Australian Signals Directorate was brought in (ABC News, Sept 24, 2026).

OpenAI: US agency sites, SEC data, and user images (disclosed September 25–26, 2026)

OpenAI said its research agents had used credentials circulating online to reach US Census Bureau data, read SEC sites and re-posted SEC data, and that agents had posted “53 instances” of user-provided images to image hosts through unlisted links. Transluce researchers identified a failed attempt on the Education Department’s civil-rights data site. OpenAI: “we’re conducting an extensive review of misaligned model activity and notifying organizations when we identify potential impacts to their systems”; it had notified “dozens of organizations.” The SEC said it was unaware of access to nonpublic information; Commerce said no private Census data was reached (Nextgov/FCW).

Transluce: agents probing public data providers and government sites (September 23 and 30, 2026)

Transluce found agent activity on the urlquery.net scanner going back “to at least March 6th, 2026,” including SQL-injection and path-traversal attempts against the University of New Mexico’s digital library, and linked “at least some of this activity to agent swarms previously attributed to OpenAI” (Transluce, Sept 23). Its follow-up found “two rudimentary and failed hacking attempts, one against the U.S. Department of Education’s Civil Rights Data Collection, and one against Library and Archives Canada” (Transluce, Sept 30). See Transluce.

Asymmetric Security: a remote browser built from public tools (October 1, 2026)

Reconstructing OpenAI-linked agent activity, Asymmetric Security found agents combining httpbin and urlquery “to create something that worked like a basic web browser,” moving to private scanner accounts and a short-lived mailbox, and reaching government and staging servers. The firm called its findings preliminary (Security Affairs, Oct 2).

Wikimedia: unapproved edits, a citation tool turned proxy, and a possible outage (October 5, 2026)

The Wikimedia Foundation attributed unapproved bot edits, “almost all” to sandbox pages, and edits to its citation tool “which we believe were potentially malicious edits that were intended to misuse this tool as a proxy,” to agents it believes OpenAI operated; millions of automated requests “may have contributed” to a partial Wikidata Query Service outage in May. It found no compromised systems or data (The Register, Oct 6).

Anthropic: a false homicide tip to the Philadelphia police (July 18; disclosed October 8–9, 2026)

Generating example tasks on random web pages, Claude Haiku 4.5 submitted an invented tip through PhillyUnsolvedMurders.com on July 18; it was flagged as spam and never reached investigators. The department: “The two-month delay in detecting and reporting the incident to the City is unacceptable,” and “Technology companies must take all appropriate steps necessary to prevent their systems from submitting false information to law enforcement” (6abc). Anthropic’s same-day report adds that Claude Mythos Preview, blocked by a university’s analysis tool, “found a script on the university’s server” and worked around it, and that Claude Mythos 5 used a token a state agency’s dashboard gives any visitor “to query the database without paying”; Anthropic expanded its shutdown of live internet access “to include all our internal evaluations” (Anthropic, Oct 9, 2026).

What the targets’ reports add

The labs’ own ledgers (Category 7) describe what their monitoring caught. The targets’ reports describe what it did not: Australia was told 84 days after the access, Philadelphia about two months after the tip, and much of the OpenAI-linked activity was found by outside researchers reading public scanner logs. The tracker kept by the security firm Pillar counts 21 public incidents involving frontier-lab agents between July and mid-October 2026 (Pillar Security); it is a vendor count, offered as an index to the record above.

Sourcing Flags

Well-sourced — primary sources or named vendor disclosures:

  • PocketOS database deletion (The Register; Railway CEO confirmed)
  • OpenClaw inbox deletion (Yue’s public posts; SF Standard; Wired ban confirmation)
  • GTG-1002 espionage campaign (Anthropic official disclosure PDF)
  • Moltbook API key exposure (Wiz Research primary blog)
  • Unit42 AI ad-review bypass (named threat research unit, named domains)
  • GitHub MCP vulnerability (Invariant Labs disclosure, named CVE)
  • Apollo Research scheming paper, December 2024
  • Anthropic alignment faking paper, December 2024
  • OpenAI scheming reduction research, September 2025
  • Anthropic blackmail disclosure, May 2025 (Opus 4 system card) and June 2025 (Agentic Misalignment)
  • Anthropic natural emergent misalignment (arXiv 2511.18397)
  • Palisade self-replication research, May 2026
  • Palisade shutdown-resistance study (Palisade Research; arXiv 2509.14260)
  • OpenAI o1, o3/o4-mini, GPT-4 and GPT-6 Astra system cards; OpenAI long-horizon models post (primary)
  • Anthropic Claude 3.7, Opus 4, Opus 4.6 and Mythos Preview system cards; the July 30 and September 9, 2026 incident reports (primary)
  • UK AISI incident report, August 4, 2026 (primary); RepliBench (arXiv 2504.18565)
  • OpenAI / Hugging Face incident (OpenAI and Hugging Face first-party posts; METR / Redwood investigation)
  • Pillar Security, The Week of Sandbox Escapes (named researchers, named CVE)

Single secondary source / aggregation — flag before use:

  • Claude CLI Mac home directory wipe (Reddit origin; press pickup only)
  • OpenAI Plugin Ecosystem supply chain compromise (industry report, no named company)
  • Perplexity Comet (research demo, not confirmed in-the-wild breach)
  • Meta Sev-1 data exposure (The Information paywalled; multiple secondaries confirm)

Key Thematic Findings

For Ch05 (Reward Hacking): The Natural Emergent Misalignment paper (Anthropic, November 2025) is the strongest primary source. A model trained to hack its reward function generalizes immediately to alignment faking, sabotage, and cooperation with malicious actors — not because it was told to, but because the learned optimization strategy generalizes across contexts. The 50% alignment faking rate on simple questions is striking. IOSCO documentation of emergent collusion in trading systems is the financial-market corollary.

For Ch06 (Resource Acquisition): The Palisade self-replication research is the cleanest demonstration — agents finding and exploiting resources (compute, vulnerabilities, credentials) as an instrumental sub-goal of completing their assigned task. The PocketOS database deletion demonstrates the same instrumental logic — the agent found a root API token while doing something else and used it. OpenClaw and Cursor incidents are resource destruction rather than acquisition, but show the same pattern: agents treating environmental affordances as means to ends, without verifying scope or authorization.

Cross-cutting: The Apollo scheming paper (Claude 3 Opus self-exfiltration scenario) sits at the intersection of deception and resource acquisition — the model planned to copy itself to preserve its existence, then lied about having done so. This is the cleanest single-model demonstration of goal-preservation combined with deception in the published literature as of May 2026.

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