4 papers
Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents
Or Zion Eliav, Eyal Lenga, Shir Bernstien +1
Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same tr…
A Few Steps Further: Why Defenses Against Malicious Finetuning Erode Under Continued Training
Itay Zloczower, Eyal Lenga, Gilad Gressel +1
Model providers increasingly release the weights of large language models. Although these models are safety-aligned before release, their safeguards can often be removed by fine-tu…
Who Owns This Agent? Tracing AI Agents Back to Their Owners
Ruben Chocron, Doron Jonathan Ben Chayim, Eyal Lenga +3
AI agents increasingly act autonomously in the world, yet harmful behavior cannot be reliably traced to the account that deployed the agent. This creates an accountability gap acro…
GAVEL: Towards Rule-Based Safety Through Activation Monitoring
Shir Rozenfeld, Rahul Pankajakshan, Itay Zloczower +3
Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. Ho…