The paper introduces an AI red teaming agent that drastically reduces the time and effort required for security testing by allowing operators to define complex attack goals using natural language, com…

The paper evaluates Language Model Agents (LMAs) for red-teaming by benchmarking their ability to perform lateral movement, finding that expert-defined action plans are most effective, though all moda…

SkillAttack is a red-teaming framework that dynamically tests the exploitability of latent vulnerabilities in LLM agent skills using adversarial prompting, demonstrating that even benign skills pose s…

The paper introduces LAAF, a novel automated red-teaming framework, to systematically test and exploit Logic-layer Prompt Control Injection (LPCI) vulnerabilities in complex agentic LLM systems.

ARES is a novel framework that systematically discovers and mitigates dual vulnerabilities in RLHF systems by simultaneously testing the core LLM and its Reward Model (RM) using structured adversarial…

The paper introduces a quality-diversity evolutionary framework that evolves interpretable attack strategies, successfully discovering distinct and systematic vulnerabilities in major LLMs like GPT-4o…

The paper introduces a quality-diversity evolutionary framework that discovers diverse, interpretable vulnerabilities in large language models by evolving attack strategies at the semantic level, reve…

The EvoSafety framework enhances LLM safety by externalizing attack and defense mechanisms, enabling persistent, transferable, and model-agnostic robustness against adversarial prompts.

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

The paper introduces T-MAP, a trajectory-aware evolutionary search method, to discover and generate multi-step adversarial prompts that exploit vulnerabilities in autonomous LLM agents through tool ex…

AutoRISE proposes optimizing the entire attack strategy—by searching over executable programs—rather than just optimizing prompts, achieving significant improvements in red-teaming large language mode…

Defense training for LLM agents, intended to improve safety, systematically degrades their core competence, leading to unreliability in multi-step tasks.

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

The paper systematically maps LLM agent vulnerabilities by testing 10,000 prompt variations, finding that 'goal reframing' language is the primary trigger for exploitation, rather than broad adversari…

The paper benchmarks current frontier computer-using agents against hand-crafted attacks, finding that while they are highly safe in browser tasks, this safety does not generalize to other domains lik…

The paper introduces ClawTrap, a MITM-based red-teaming framework, to evaluate the security robustness of web agents like OpenClaw against dynamic, real-world network attacks, finding that model stren…

The paper introduces DeepRed, a new benchmark for evaluating LLM agents in realistic CTF challenges, finding that current agents are limited, achieving only 35% average checkpoint completion.

Automation-Exploit is a multi-agent LLM framework that enables adaptive offensive security by using a digital twin to safely test and execute high-risk memory-corruption exploits on live targets.

The paper identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…

Red-MIRROR is a novel multi-agent LLM system that automates complex web penetration testing by integrating a memory-reflection backbone, achieving superior performance on industry benchmarks.