activity
20242026
most citedAI Agents Enable Adaptive Computer Worms

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2026

Backdoor Decontamination Dynamics in LLM Agents

Gabriel Huang, Abhay Puri, Léo Boisvert +4

Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are never met during testing. Assuming defender…

cs.CR20262 cited

AI Agents Enable Adaptive Computer Worms

Jonas Guan, Tom Blanchard, Hanna Foerster +3

A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, a…

cs.CR2026

Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?

Rishika Bhagwatkar, Kevin Kasa, Abhay Puri +5

AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cause unintended or harmful behavior. Inspi…

cs.CR2025

DoomArena: A framework for Testing AI Agents Against Evolving Security Threats

Leo Boisvert, Mihir Bansal, Chandra Kiran Reddy Evuru +9

We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework and integrates easily into realistic ag…

cs.AI2024

TapeAgents: a Holistic Framework for Agent Development and Optimization

Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang +10

We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents…