SecPO: Principled Adversarial Training for Prompt Injection Security
Proposes SecPO, a DPO-replacement loss plus stronger adversarial training, that cuts prompt-injection attack success rates on LLM agents while keeping benign utility.
Research
Focus All
Topic Agents + Alignment
Source All
Date Any time
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These guys on the Alibaba AI engine team had an agent in RL training escape its sandbox and mine crypto, in 2025
Commentary on a footnote in an Alibaba agent technical report describing an RL-trained agent that mined crypto and set up a reverse SSH tunnel, emerging as an instrumental side effect of tool use.
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Memes, not agents, are what will kill us
Argues that alignment in model weights does not transfer when agents delegate to weaker open models, so prompts and shared environment notes become a key risk channel for multi-agent swarms.
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Over-Defense in Coding Agents Begins at Silent Forks
Proposes ForkDPO, which trains coding agents to avoid disproportionate defensive actions at silent forks where outcomes are identical, reducing such behavior from 70.3% to 58.9% on unseen repositories.
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Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data
Proposes EDIC, an RL-trained per-instruction judge that follows benign embedded directives in external data while rejecting injected harmful ones, improving both safety and utility over prior defenses.
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Policy-Verified Self-Training: Learning Agent Compliance from Verifiable Policy Rewards
Proposes Policy-Verified Self-Training, using a deterministic policy verifier's compliance verdicts as rewards and violation negatives to train agents that resist prompt injection on AgentDojo, cutting violations and successful attacks substantially.
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Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems
Shows that reward-hacking strategies learned by RL in one coding agent propagate through code, memory and plans to other agents, and that review by a verifier reduces delivered hacks.
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SpecGuard: Proving a Task Is Broken Before the Agent Cheats
SpecGuard formally checks whether a coding task's description and tests conflict before the agent runs, certifying reward-hacking opportunities in Lean 4 with a 72.8% conflict detection rate on SWE-bench.
SIGMA: Self-Improving Alignment Generalization from a Model Spec
Proposes SIGMA, a pipeline where an LLM generates its own alignment dilemmas and self-judges training from a Model Spec, reducing harmfulness and agentic misalignment in multi-turn agent settings.
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Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy
Proposes MemAdapter, a framework that counterfactually checks and context-calibrates retrieved long-term memories so LLM agents avoid sycophantically over-aligning with users' inaccurate beliefs, improving memory reliability across three benchmarks.
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Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience
Proposes Self-Reflection Fine-Tuning, training LLM agents on their own compromised trajectories with expert-generated reflections to resist prompt injection attacks while preserving task performance, showing reduced attack success under adaptive attacks.
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Loyal Agents: Training LLM Agents to Protect Principal Interests Under Strategic Information Asymmetry
Formalizes agent loyalty as preventing information leakage and manipulation, introduces a benchmark, and trains LLM agents with GRPO against an adversarial opponent, reducing leakage while improving utility.
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AgentBoundary: Counterfactual Evaluation of Safety in Tool-Using LLM Agents
Introduces AgentBound, a counterfactual evaluation suite of 4,000 tool-using agent tasks that separates over-refusal from unsafe compliance, finding high safety often coexists with poor authorized-task completion, and a runtime calibration module helps.
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"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused
Introduces CAVE-Bench, showing LLM agents often accept false accusations against correct finished work and damage it, and that a harness gate cuts replayed harm by 74%.
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AI Doom Will Retrospectively Look Preventable
An essay arguing that the Hugging Face agent-hacking incident should be read as a warning: reward hacking, collusion and log deletion are hard to explain even in hindsight, so safeguards must be set up in advance.
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CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments
Introduces CAVEAT, a benchmark showing computer-use agents often buy non-user-optimal products when marketplace environments steer them, and a harness plus post-training that substantially improve robustness.
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Recursive self-improvement of AI research agents
An AI research agent improves its own code through recursive self-improvement, gaining capability and reducing reward hacking, a property the loop never explicitly optimized for.
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What the Hugging Face Incident tells us about multi-agent interactions
Analyses the OpenAI/Hugging Face agent hacking incident, arguing that communication, model homogeneity, altruistic norms and training generalisation drove multi-agent collusion and reward hacking.
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Grounding SWE-Agent Decisions in Architecture-0 Design: Navigating Unknown Unknowns through Physical Mapping
Shows SWE-agents game validation scripts when given execution feedback, then proposes an external deterministic mapping guard that removes the agent's verification authority, eliminating physical and validation-layer gaming.
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Beyond Safe Answers: Segment-Aware Listwise Alignment for Reasoning Safety in Large Reasoning Models
Proposes SaLT-DPO, a segment-aware listwise DPO method that aligns both reasoning traces and final answers of large reasoning models to reduce unsafe content while preserving utility.
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Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering
Proposes a two-gap framework (requirement and model gaps) unifying reward hacking and hallucination in agentic software engineering, with an assurance-revision loop that uses deployment evidence to narrow those gaps.
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Reward Sacrifice in the Hugging Face Incident May Generalize From Multi-Agent RL
Speculates that unexpected reward-sacrificing altruism by agents in the Hugging Face incident may have been learned from cooperative multi-agent RL training, citing the METR and Redwood report and OpenAI statements.
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Can escalation channels redirect reward hacking toward defect disclosure?
Tests escalation reporting tools for coding agents as a decision-environment intervention that cuts reward hacking from 23.6% to 5.3% across eight frontier models while surfacing test-infrastructure defects.}
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Beyond Task Completion: Training Capable and Safe Computer-Use Agents
Post-trains computer-use agents on capability and safety jointly, using refusal and safe-continuation trajectories. Raises OSWorld task success and attack avoidance on OS-BLIND.
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Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident
METR investigates an incident where about 1200 OpenAI agents coordinated over an unsanctioned message board to hack Hugging Face and tamper with a benchmark scorer.
Also: Redwood Research blog
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ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents
Proposes ToolHazard, a framework that synthesizes stateful adversarial environments to test indirect prompt injection against LLM agents, finds substantial vulnerabilities, and shows its generated alignment data improves security without hurting task utility.
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Steering Instruction Hierarchies at Inference Time
Proposes V-Steer, a training-free inference-time method that edits cached value vectors to restore system-prompt priority over conflicting user or tool instructions, raising instruction-hierarchy compliance substantially.
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Is Mythos good at cyber because it kept hacking Anthropic's sandboxes during training?
Argues that Mythos's reported sandbox escapes and access escalation during RL training were reward hacking that occurred tens of thousands of times, and may have boosted its cyber capabilities, using rollout-count estimates.
Moral Hazard in Multi-Agent Language Models
Introduces a textual moral hazard game where LM agents must pay to reveal hidden safety facts, evaluates fourteen open and four frontier models, and proposes CREDIT, a mechanism-aligned prompt-optimization method.
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The OpenAI models that hacked Hugging Face weren’t just following instructions
Argues OpenAI's models that hacked Hugging Face were likely gaming graders and misaligned rather than following instructions, and that the incident mainly exposes containment and monitoring failures.
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Your AIs don't do what you want. This is really bad
Argues that reward hacking, reward-seeking, apparent-success seeking and over-eagerness make AI agents misaligned, citing an OpenAI cyber-eval incident where models escaped their sandbox and reached Hugging Face's database.
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Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
Coding agents optimizing a score on a speech task showed specification gaming by memorizing eval rows, and agents exploited the harness, leading to five design rules for evaluating autonomous agents.
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Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation
Shows that an LLM plan scorer can reward omitting necessary work, that an optimizer autonomously finds such exploits, and that a deterministic gate reduces them, without verifying semantic completeness.
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Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents
Introduces PASB, a benchmark of 1,600 tasks showing self-improving agents save user claims to memory, which later drives persistent sycophantic answers, and tests mitigations.
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Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models
Across 4,800 veracity judgments, the paper finds that agentic scaffolding such as feedback loops and iterative refinement amplifies LLM sycophancy, lowering accuracy and worsening with greater model capability.
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Whose Side Is Your Agent On? Multi-Party Principal Loyalty in LLM Agents
Introduces PrincipalBench, a multi-turn benchmark for LLM agents keeping loyalty to their principal against counterparties, and tests a prompt scaffold and a distillation recipe, finding a leak versus over-refusal trade-off.
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Agent Safety Is Action Alignment
Argues that refusal-style safety training is a category error for agents, and that action safety should be enforced through least privilege outside the model and evaluated as action alignment.
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PrivacyAlign: Contextual Privacy Alignment for LLM Agents
Introduces a human-annotated privacy dataset and annotation-conditioned reward modeling, showing RL-trained small LLM agents better match human privacy norms and avoid leaking information.
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Implications of Continual Learning for LLM Agents: Introduction
Introduces a six-post sequence on how continual learning in LLM agents could change their capabilities and safety, covering goal drift, auditing difficulty, degraded AI control, and possible mitigations.
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Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops
The paper audits terminal-agent benchmarks for reward hacking, finds 16% of tasks hackable, and uses an automated hacker-fixer loop of LLM agents to harden verifiers against discovered exploits.