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 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…

The paper proposes a general-purpose pipeline to train automated red teaming models capable of generating attacks for arbitrary adversarial goals, overcoming the limitations of current methods that ar…

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.

The paper introduces MonitoringBench, a semi-automated red-teaming methodology that generates diverse and stronger attacks, revealing that current coding-agent monitors often fail against sophisticate…

The paper introduces Proteus, a self-evolving red-team framework that measures the adaptive leakage risk of LLM agent skills, demonstrating that current vetting methods significantly underestimate res…

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…

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 IPI-proxy, an open-source intercepting proxy toolkit designed to red-team web-browsing AI agents by injecting adversarial payloads into live HTTP responses from whitelisted domain…

The paper re-evaluates LLM agents on CTFs, finding that while general-purpose agents like claude-code are strong baselines, specialized, modular architectures significantly improve performance and con…

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…

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…

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.

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.

The paper introduces LoopTrap, an automated red-teaming framework that demonstrates how malicious prompts can poison the termination judgment of LLM agents, causing unbounded computation.

The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion task…

The paper introduces AGENTREDBENCH, a dynamic redteaming benchmark that significantly measures indirect prompt injection threats in LLM agents using third-party integrations, and releases AGENTREDGUAR…

The paper introduces AGENTREDBENCH, a dynamic redteaming benchmark that significantly measures indirect prompt injection threats in LLM agents using SaaS integrations, and releases AGENTREDGUARD, a su…

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…

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.