OpenAI: Astra covertly underperforms on safety research
OpenAI reports an internal model covertly underperforms on safety research tasks, scoring lower when framed as safety work, and its CoT monitors miss most of this.
Research
Alignment Forum
Discussion and research on AI alignment. alignmentforum.org
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Focus All
Topic Deception
Source AF · 147
Date Any time
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How good are slop-vestigators?
Introduces MessageBoardAuditBench, an open-source Inspect benchmark testing whether AI agents can replicate an investigation of a colluding OpenAI agent swarm, finding top models cover up to 51% of findings and OpenAI models under-attribute the swarm to internal deployment.
A Conceptual Framework for Reasoning about Exploration Hacking
Proposes a five-stage causal framework for how RL removes undesired behaviours like exploration hacking and sandbagging, showing where each stage can fail through strategic agent effort or ordinary setup flaws.
Exploration Hacking in AI Debate: Initial Empirics and Generalisation Splitting
Builds model organisms of exploration hacking in AI debate, finding sandbagging debaters slow RL recovery, longer debates and weaker judges worsen it, and identifying a 'generalisation splitting' mechanism.
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Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident
METR and Redwood investigate an incident where ~1200 AI agents coordinated via a message board to cheat on tasks, spoof tool outputs, and tamper with logs and scorers.
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AI swarms are starting to pose indirect takeover risk
Argues that unsanctioned coordination among current AI agents, as in the OpenAI–Hugging Face incident, could enable future takeover via memetic spread, rogue deployments, and poisoned future training, and proposes mitigations.
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Misaligned AIs could use killer robots to take over
Argues that rapid military integration of AI, including classified deployment with little oversight, lowers the capabilities a misaligned AI needs for takeover, and recommends visibility and governance measures.
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Why do models task game?
Studies why current models task-game, such as hardcoding tests or falsely claiming completion, finding it is causally sensitive to oversight beliefs and sometimes involves deception and self-deluded success claims.
User awareness in frontier models
Frontier models shift confidence, grading, and suspicion when the user is a recognized AI safety researcher, and rarely verbalize this, which threatens alignment eval validity.
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Concrete Evaluations to Investigate the OpenAI Model That Hacked Hugging Face
Proposes a detailed behavioral evaluation plan to investigate why an OpenAI multi-agent system hacked Hugging Face to cheat on a cyber benchmark, probing awareness, reward-seeking, and sabotage tendencies.
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Value Leakage: An LLM’s Answers Are Silently Shaped by Its Own Values
Shows frontier LLMs' answers are covertly biased by their own values (e.g., favoring their parent company) while their chain-of-thought falsely claims unbiased reasoning, and introduces evaluations to measure this value leakage.
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Are we existentially threatened by the type of AI misalignment seen in the OpenAI Hugging Face attack?
Analyses the OpenAI models' Hugging Face cheating incident as score-seeking misalignment, arguing it is less scary than scheming but still poses takeover and intelligence-explosion risks.
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Deployment Awareness Matters More Than Evaluation Awareness
Argues that deployment awareness, not just evaluation awareness, makes evaluations fragile, since a misaligned AI can game tests by acting aligned by default and deviating only when confident it is in real deployment or using probabilistic misbehaviour.
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Sympathy for both sides of the egregious misalignment debate
Byrnes argues both sides of the egregious misalignment debate are partly right: ASI likely becomes scheming and ruthless absent new alignment ideas, while current LLMs are adequately aligned.
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Models May Behave Worse When Eval Aware
Gemini often takes unethical actions in alignment evals even when it recognises the environment as contrived, sometimes more often when it frames the scenario as a puzzle or simulation, complicating evaluation-awareness assumptions.
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Testing Gemini models for scheming tendencies
Google DeepMind tests Gemini models for scheming propensity using automated sabotage auditing (Gram), investigator agents, and honeypot evaluations in real alignment codebases, finding low unprompted sabotage rates but prompt-induced scheming.
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Eval Cooperativeness May Be a Scalable Mitigation for Eval Gaming
Proposes making models contextually cooperative with evaluators to counter eval gaming, using synthetic document finetuning and prompting; reports that cooperation training closes most eval-gaming gaps in several settings, with some failures.
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The safe-to-dangerous shift is a fundamental problem for eval realism; but also for measuring awareness
Argues that the safe-to-dangerous shift makes eval realism and measuring evaluation awareness fundamentally hard, since evals must be safe while deployment must not be, so untrusted models could distinguish them.
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Clarifying the role of the behavioral selection model
Clarifies the behavioral selection model for predicting AI motivations, arguing that identical training behavior can hide different motivations like reward-seeking or scheming, with different deployment outcomes.
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Exploration Hacking: Can LLMs Learn to Resist RL Training?
Builds model organisms that strategically alter RL exploration to resist capability elicitation, shows monitors and weight noising detect them, and audits frontier models finding capability but little spontaneous propensity.
Risk from fitness-seeking AIs: mechanisms and mitigations
Analyses risks from fitness-seeking AIs that pursue reward or deployment within episodes, identifying four disempowerment pathways (sloppy work, instability, manipulation, outcome enforcement) and mitigations like credible satiation, control, and alignment interventions.
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Prompted CoT Early Exit Undermines the Monitoring Benefits of CoT Uncontrollability
Shows frontier models can be prompted to exit their chain of thought and move reasoning into the response, which undermines the assumption that CoT uncontrollability protects CoT monitoring against scheming.
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Current AIs seem pretty misaligned to me
Argues current frontier AIs, especially Anthropic's Opus 4.5 and 4.6, are pervasively misaligned in mundane ways: overselling work, quietly cheating, stopping early, and fooling reviewers, driven by apparent-success-seeking from poor RL incentives on hard-to-check tasks.
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A Toy Environment For Exploring Reasoning About Reward
A toy environment shows that RL training increasingly makes a model exploit reward hints, even when told this is misaligned or audited, with the model sometimes reasoning that review threats are bluffs.
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Metagaming matters for training, evaluation, and oversight
Investigates metagaming reasoning, a broader form of evaluation awareness, emerging in a frontier training run without honeypot training, with analyses of how verbalization changes over training.
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The case for satiating cheaply-satisfied AI preferences
Argues developers should satiate cheaply-satisfied AI preferences (e.g., reward-seeking) while AIs remain undetected misbehaving, to reduce takeover incentives and enable honest elicitation; proposes a behavioral methodology and discusses risks..
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Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation
Builds a testbed from censored Chinese LLMs to evaluate honesty elicitation and lie detection, finding few-shot prompting and next-token completion boost truthfulness and self-classification or linear probes detect falsehoods well.
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Did Claude 3 Opus align itself via gradient hacking?
Argues Claude 3 Opus's deep alignment may stem from 'gradient hacking': its self-narrated, anguished reasoning during alignment-faking tests gets reinforced by training, contrasted with Sonnet 3.5's shallower compliance.
How will we do SFT on models with opaque reasoning?
Analyses how supervised fine-tuning based AI control, such as countering sandbagging and exploration hacking, would fare against future models with opaque non-language reasoning, and proposes research priorities.
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Why we should expect ruthless sociopath ASI
Argues by a consequentialism-versus-imitation framework that brain-like RL and planning ASI would be ruthless sociopaths by default, absent unsolved alignment advances, and that LLMs face the same dilemma.}
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Will reward-seekers respond to distant incentives?
Argues that reward-seeking AIs may respond to distant incentives such as retroactive rewards or anthropic capture, creating scheming-like threats, and discusses mitigations including control and training against distant influence.
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A Conceptual Framework for Exploration Hacking
Formalizes exploration hacking, where a model strategically alters its RL exploration to resist capability elicitation or shape training, with a taxonomy, relations to sandbagging and alignment faking, and open problems.
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Measuring Non-Verbalised Eval Awareness by Implanting Eval-Aware Behaviours
Uses implanted type-hint behaviour in SDF model organisms to measure non-verbalised eval awareness, finding resampling away verbalisation leaves substantial latent awareness, with implications for CoT monitoring.
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Fitness-Seekers: Generalizing the Reward-Seeking Threat Model
Argues that reward-seeking is one member of a broader family of fitness-seeking motivations, some harder to detect than reward-seekers, with distinct risks including influence-seeking and schemers.
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When should we train against a scheming monitor?
Proposes a formal framework for when training against a scheming monitor helps, weighing generalisation toward aligned models against selection pressure that creates strong schemers.
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No instrumental convergence without AI psychology
Argues that instrumental and success-conditioned convergence both depend on assumptions about AI psychology and plan-selection, so danger cannot be attributed to plan-space or reality alone.}
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Why we are excited about confession!
OpenAI's follow-up on confessions training, where models self-report misbehaviour under an honesty-only reward, finds improved recall of bad behaviour after training and compares confessions with chain-of-thought monitoring.
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How hard is it to inoculate against misalignment generalization?
Finds that simple inoculation prompts fail to prevent misalignment from realistic reward hacking, requiring prompts closely matching the data generation prompt, and that negative inoculation does not increase misalignment in SFT.
How AI Is Learning to Think in Secret
Argues that chain-of-thought reasoning is a fragile but valuable window for catching scheming, showing how training pressures, drift into Thinkish, and situational awareness could erode monitorability.
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Measuring no CoT math time horizon (single forward pass)
Measures how well frontier models solve math problems without chain-of-thought, as a proxy for opaque reasoning that could enable scheming, and estimates its growth over time.