arXiv Machine Learning
Aug 31

Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap

The paper shows that post‑training quantization can introduce backdoors in large language models that are not detected by source‑precision checks. By formalizing the validation‑deployment gap with Quantization Behavioral Equivalence Classes (QBECs), the authors demonstrate that models can pass full‑precision tests yet exhibit malicious behavior after INT8 or 4‑bit compression. Experiments on machine translation and political stance classification reveal significant corruption and ideological shifts, and cross‑quantizer analysis indicates that attack persistence depends on the quantization scheme and architecture rather than just bit‑width.

By Jacopo Dardini, Claudio Stanzione, Giordano Col\`o, Giuseppe Fenza
arXiv AI
Sep 16

Universal Defenses for Tool-Integrated LLM Agents Against Adversarial Attacks

The paper proposes universal, tool‑based defenses for large language model agents that use external tools, addressing four types of adversarial attacks: direct and indirect prompt injection, memory poisoning, and backdoor attacks. Two main defenses are introduced: Attacker Tool Filtering, which uses anomaly detection to remove suspicious tools, and Normal Tool Recalling, which restores the agent’s original toolset before planning. The authors also add prompt‑based defenses such as Chain‑of‑Thought prompting and self‑reflection, and demonstrate that these methods dramatically lower attack success rates—often to 0%—across multiple open‑source and proprietary LLMs while maintaining or improving task performance.

By Xiaoyan Li, Yunli Wang
arXiv Machine Learning
Oct 28

All-in-One: A Composable Attack on LLM Transformations

The paper introduces a unified attack framework that can simultaneously target multiple large language model (LLM) transformations such as quantization, pruning, and model merging. By treating each transformation as a weight perturbation, the authors jointly train a model to perform benignly before transformation and maliciously after any one of the transformations is applied. Experiments across various attack scenarios and model families show high success rates, illustrating that a single adversarial model can exploit diverse deployment-time transformations.

By Xiaohua Zhan, Kazuki Egashira, Robin Staab, Mark Vero, Martin Vechev