AGENTQ: Quantization-Conditioned Backdoor Attacks on LLM Agents
Quantization is one of the default deployment paths for open-weight LLM agents, but it is not behavior-preserving: an adversary can release a full-precision checkpoint that passes audits yet misbehaves once quantized, termed as quantization-conditioned attack (QCA). Prior QCA work targets free-text generation, where harm is mediated by a human reader. In contrast, the agentic setting poses a more severe risk: the triggered payload is a structured function that can be executed without human oversight. We present the first study of QCA against LLM agents. We find that directly adapting prior backdoor-injection methods can produce malicious behavior after quantization, but substantially degr...
| Authors | Xiaoqun Liu, Qiben Yan |
|---|---|
| Primary category | cs.CR |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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