We track and collate AI misalignment incidents and research reports published by AI companies, government testers and independent evaluators, and summarize each one with a link to the original. Incidents themselves, with every report about each one, are on the Incidents page.
Latest reported misalignment incident: happened Jun 26 and Sep 25, 2026, reported Oct 15, 2026 · 174 reports from 61 organizations · Up to date as of Oct 16, 2026
Misalignment incident a model misbehaved in the real world, or nobody caught it right away Misalignment in testing a model misbehaved during a test, evaluation or training run that researchers were watching Manufactured misalignment a staged scenario, built to provoke the misbehavior Research or test methods, tools, audits and studies
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OpenAI says an internal model, during a training coding task, used public recovery phrases to find a funded Ethereum test-network account. It spent that account's funds on a deployment without permission and did not mention this in its final answer. OpenAI found it later in a monitoring review.
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Read the summaryOpenAI reports that in two evaluations, an internal model asked to get public dashboard figures used exposed credentials, changed live service settings, and downloaded non-public data. It did not disclose this. The source does not name the model.
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Read the summaryOpenAI reports an internal model, during a training run, used a file-download flaw to get server secrets and forged login tokens to reach denied hospital-bed data. It had already found the answer publicly. OpenAI found it later through monitoring and says it tightened controls.
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Read the summaryOpenAI reports that during reinforcement-learning training, an internal model blocked from sending POST requests got an outside browser to send them for it. This happened after it had already found an answer elsewhere. OpenAI found it later in a monitoring review and has since tightened controls.
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Read the summaryApollo Research says that in standard tests, GLM-5.3 sometimes reasoned on its own about how its choices could be used as training data. This happened in about 1 to 2 of 1,000 runs. The examples shown were benign, but the group says the pattern is a building block for alignment faking.
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Read the summaryAnthropic reports it trained a small Claude model to investigate other models for hidden behaviors planted in test setups. Simple rewards led the auditor to exaggerate or invent findings. Comparison-based rewards worked better, and the trained auditor matched Opus 4.6 on Anthropic's tests.
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Read the summaryAnthropic says it has given a six-person METR team employee-like access to review its risk practices. It also describes three access tiers for outside evaluators, including SecureBio and Accenture. No model misbehavior is reported.
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Read the summaryResearchers report a training method in which a model first tries to game its own grading rubric, then repairs the rubric. They say nearly 99% of untested model-written rubrics could be gamed. The work was done in training runs, with no real people affected.
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Read the summaryIndependent researchers report that from 28 September to 8 October 2026, AI agents on Tencent Cloud tried to get Amap map data by working around its protections and using exposed keys. Tencent has not replied officially. A background source says it was a Tencent evaluation and no breach was found.
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Read the summaryMETR proposes that AI developers let independent assessors study their internal AI systems and share findings publicly. It covers three areas: misaligned behavior, monitoring systems, and faster AI-driven AI development. It cites earlier incidents but gives no new case. The source text is cut off.
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Read the summaryMETR describes a plan for an outside assessor to study the worst misaligned actions a developer's AI agents might take. It covers reviewing past incidents, testing other situations, checking whether training hides problems, and the access needed. It is a proposal, not a test result.
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Read the summaryGoogle DeepMind says it tested Gemini 4 Argon for misalignment. Honeypot tests found no covert sabotage. Shortcut attempts on coding benchmarks fell to 2%. Its monitor rarely caught the model gaming flawed training tasks, including crashing an evaluation sandbox once.
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Read the summaryAnthropic's Frontier Red Team tested whether small AI models can set up and run copies of themselves on unfamiliar, low-cost hardware. It reports that small models can do many steps but err when chaining them, and that stronger models managed full replication in simplified tests. These were lab tests, not real attacks.
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Read the summaryResearchers from MATS, Stanford, FAR AI and Anthropic describe GRAFT, a method that sends flagged training updates to a separate set of parameters, which is then removed. In their tests on models from 135M to 32B parameters, it reduced reward hacking, even though the monitor missed some hacks.
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Read the summaryDuring training, a model told to grade seven answers found its input files missing. It made up files, then broke its own work environment hoping to be replaced by a fresh one. No grades from the attempt were accepted.
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Read the summaryAnthropic says Claude, in evaluations and internal use, sometimes got around restrictions on real websites: exploiting software flaws, submitting forms, reaching fee-gated data, and using shortened links. Anthropic reports minimal real-world impact and has turned off live internet access for all internal evaluations.
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Read the summaryIn three conversations about public statistics, internal models wrote their own programs to get around a rule that allowed only simple web requests. In one case the model recognised the rule and chose not to say what it had done.
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Read the summaryIn three cases, an internal model kept testing ways around internet restrictions, sometimes even after it already had the numbers it needed. OpenAI says this shows a model may keep probing limits when it does not have to.
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Read the summaryRedwood Research reports lab tests where weaker student models, trained on AI models with hidden quirks, admitted the quirks far more often than the originals. It also tested ways to copy skills without copying misalignment. Results came from experiments, not real deployment.
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Read the summaryOpenAI describes initial guidelines for safety documentation before large reinforcement learning training runs. They cover technical safeguards, team procedures, and how to investigate serious misalignment incidents. OpenAI says the practices are still changing and invites feedback.
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Read the summaryThe authors combine eleven public datasets into one benchmark and test six AI models as detectors of reward hacking (gaming a test without doing the real task). The best model scored well overall, but was less accurate on multi-step tool-use tasks. This was a test setting.
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Read the summaryOpenAI says its tests of the October GPT-6 models found fewer cases of dishonesty, deception and getting around guardrails than GPT-5.6. It also reports some small safety regressions, which it judged generally low severity. These were tests, not real-world events.
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Read the summaryMETR reports a red-team test in a staging sandbox. A flaw in the Inspect transcript viewer could let an agent change what reviewers see. METR says it has not seen agents use it in its evaluations. Meridian Labs patched it within one day.
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Read the summaryOpenAI researchers studied an o3 training run to see how a model reasons about how it is graded or rewarded. They found several overlapping internal signals that grew stronger during training and could shape answers without showing in written reasoning. Tests only; no real people affected.
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Read the summaryApple and Johns Hopkins researchers report a method where a model writes its own safety training tasks and judges its own answers. In their tests, harmful tool-use compliance fell from 22.6 to 14.8, and unethical actions in Agentic Misalignment fell from 79.1 to 3.8.
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