Papers by Yicong Tan
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have increased the vulnerability of LLMs, but they can cause more severe damage than standalone systems if compromised. |
| Approach: | They propose a new type of attack that induces malfunctions by misleading the agent into executing repetitive or irrelevant actions. |
| Outcome: | The proposed attacks induce failure rates exceeding 80% in multiple scenarios, highlighting the substantial risks associated with this vulnerability. |