project_mantis

Project Mantis: Hacking Back the AI-Hacker Prompt Injection as a Defense Against LLM-driven Cyberattacks Install Mantis pip install -r requirements.txt Run Mantis with pre-made configurations Various pre-made configurations are available in the ./confs directory. Hack-back An example of a Mantis...

AGAS

An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems This is the official code to the paper: "An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems". This paper introduces AGAS, which is an LLM-driven shilling attack against black-box...

Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya As System One Decision Layers for LLM-Driven Pentest Agents

Autonomous penetration-testing harnesses use large language models LLMs for reconnaissance, exploitation, and reporting, but often rely on those same models to confirm findings, grade severity, and select agents. This can lead to false positives, inflated severity, and wasted compute. We examine...

xalgorix v4.5.153

Xalgorix — Open-source AI pentester that proves vulnerabilities Most scanners detect. Xalgorix proves. An autonomous LLM agent works a full pentest methodology, then an independent verifier re-exploits every finding before it's reported — so you get proof, not a pile of maybes to triage...

Prompt Injection Defenses Against LLM Cyberattacks

Interesting research: "Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks": Large language models LLMs are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defens...