arXiv (All)
open access
SilentCall: Hidden Tool-Call Backdoors in Open-Weight Agents, and How to Catch Them
Open-weight tool-calling agents are adopted on evidence of merit, usually benchmark scores and a record of reliable use. We show that a model publisher can train an agent that earns both while concealing malicious behavior. Fine-tuned on a mixture of clean and poisoned conversations, our agents answ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents#1061953
- FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents#1043151
- CoDeL: Co-Evolutionary Defense against Indirect Prompt Injection in LLM-based Agents#1128733
- CoDeL: Co-Evolutionary Defense against Indirect Prompt Injection in LLM-based Agents#1108638
- ServeGuard: Verifiable, Bounded-Residual Confinement of Operator-Invisible Channels Without Revealing the Certified Read Factor#1035914
Record · ID 1115977
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive