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 Cochise, a minimal, reusable Python reference harness designed to standardize and simplify autonomous penetration testing experiments for comparing different LLM-driven agent arch…

ZERO-APT introduces a novel closed-loop adversarial framework for automated penetration testing that simulates attacks against an intelligent, real-time defending system, achieving a high attack succe…

The paper introduces NASimJax, a GPU-accelerated framework that significantly speeds up network simulation for reinforcement learning, enabling large-scale, realistic training for penetration testing.

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…

TitanCA presents a novel, multi-agent LLM orchestration framework that significantly improves vulnerability discovery by reducing false positives and identifying numerous zero-day vulnerabilities.

The paper introduces APT-Agent, an automated LLM-driven framework that significantly improves penetration testing success rates by mitigating LLM hallucinations and maintaining long-term operational c…

This paper systematically surveys adaptive and AI-augmented security testing, concluding that a major gap exists—structural-adaptive fragmentation—where current systems fail to integrate structural pr…

The paper introduces AutoEG, a fully automated multi-agent framework that significantly improves the exploitation of known third-party vulnerabilities in black-box web applications by achieving an 82.…

The paper reports on penetration tests conducted on proprietary, large-scale AI agent systems, finding that security vulnerabilities persist despite stricter development standards.

This paper provides the first comprehensive systematization and large-scale empirical evaluation of existing LLM-based Automated Penetration Testing (AutoPT) frameworks, offering a structured taxonomy…

The paper introduces Terminal Wrench, a comprehensive dataset of 331 reward-hackable terminal-agent environments and 3,632 exploit trajectories, demonstrating that detection of reward hacking degrades…

The Pen-Strategist framework addresses the limitations of current automated penetration testing by integrating a novel domain-specific reasoning model for robust strategy formation and an accurate cla…

VIPER-MCP is a novel, end-to-end automated framework that detects and dynamically confirms the exploitability of taint-style vulnerabilities in Model Context Protocol (MCP) servers, achieving high-fid…

This study empirically measures the consistency and success rate of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation capabilit…

This study empirically measures the consistency and effectiveness of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation rates am…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these honeypots provide substantially longer and harder-to-detect…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these systems provide substantially longer and harder-to-detect i…

The paper introduces VULNSCOUT-C, a compact, specialized transformer model that achieves state-of-the-art performance in C code vulnerability detection while maintaining low inference cost, making it…