Agent Hacks Agents: Autoresearch Discovers Vulnerabilities in Production Agents

arXiv:2607.11698 · cs.CR, cs.AI · Submitted 2026-07-13 · Read on arXiv

cs.CR, cs.AI

Submitted: 2026-07-13

Updated: 2026-09-26

Code: https://github.com/henrymao2004/Auto-research-red-teaming-in-sleep

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 47× in this paper · explained in 2 episodes
• Provenance — 24× in this paper · explained in 3 episodes
• Core — 16× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Vulnerability Concept Graph — 7× in this paper
• Ablation — 6× in this paper · explained in 1 episode
• Trace — 6× in this paper · explained in 2 episodes
• Reward Hacking — 5× in this paper · explained in 8 episodes
• AI Agents — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Agent Hacks Agents — 2× in this paper
• Agent Harness — 2× in this paper · explained in 1 episode
• Failure Modes — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Arbiter — 1× in this paper · explained in 1 episode
• Fidelity — 1× in this paper · explained in 2 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
• CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures
• DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
• IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization
• RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search
• AJAR: Adaptive Jailbreak Architecture for Red-teaming
• How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
• AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents
• AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
• Automated Hypothesis Validation with Agentic Sequential Falsifications
• T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search
• AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
• ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis
• ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs
• CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning
• The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
• David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learning
• Measuring Agents in Production
• Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

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