arXiv Machine Learning

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks

arXiv:2606. 28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Conditioned Backdoor (QCB) attacks.

arXiv AI

Jun 4

Widening the Gap: Exploiting LLM Quantization via Outlier Injection

arXiv:2605. 15152v2 Announce Type: replace-cross Abstract: LLM quantization has become essential for memory-efficient deployment.

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

llms efficiency

arXiv AI

Sep 15

AGENTQ: Quantization-Conditioned Backdoor Attacks on LLM Agents

arXiv:2609.14060v1 Announce Type: cross Abstract: 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-pr...

By Xiaoqun Liu, Qiben Yan

llms agents fine-tuning efficiency safety

arXiv AI

Jun 4

TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

arXiv:2602. 06911v2 Announce Type: replace-cross Abstract: As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications, whether accidental or intentional, becomes critical to minimize risks.

By Saad Hossain, Tom Tseng, Punya Syon Pandey, Samanvay Vajpayee, Matthew Kowal, Nayeema Nonta, Samuel Simko, Stephen Casper, Zhijing Jin, Kellin Pelrine, Sirisha Rambhatla

llms fine-tuning benchmarks safety

arXiv AI

Sep 1

Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors

arXiv:2604.27426v2 Announce Type: replace-cross Abstract: Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although "local...

By Zi Li, Tian Zhou, Wenze Li, Jingyu Hua, Yunlong Mao, Sheng Zhong

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 4

Bypassing Prompt Guards in Production with Controlled-Release Prompting

arXiv:2510. 01529v3 Announce Type: replace Abstract: Ball et al.

By Jaiden Fairoze, Sanjam Garg, Keewoo Lee, Mingyuan Wang

llms safety

arXiv AI

Jun 3

Patcher: Post-Hoc Patching of Backdoored Large Language Models

arXiv:2606. 02995v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that bypass safety mechanisms.

By Anjun Gao, Yueyang Quan, Yufei Xia, Zhuqing Liu, Minghong Fang

llms fine-tuning safety

arXiv Machine Learning

Jun 5

Steering Vectors are an Adversarial Attack Surface

arXiv:2606. 05958v1 Announce Type: new Abstract: Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning.

By Abzal Aidakhmetov, Donato Crisostomi, Tommaso Mencattini, Adrian Robert Minut, Iacopo Masi, Emanuele Rodol\`a

llms fine-tuning multimodal safety

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

llms nlp fine-tuning efficiency safety

arXiv AI

Jun 11

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

arXiv:2606. 11817v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code.

By Yitong Zhang, Shiteng Lu, Jia Li

llms benchmarks safety

arXiv AI

Jun 11

Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models

arXiv:2606. 11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally costly.

By Malikeh Ehghaghi, Bogl\'arka Ecsedi, Marsha Chechik, Colin Raffel

llms reinforcement-learning multimodal benchmarks safety

Hugging Face Trending Papers

Jul 22

Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into inference-time detection or training-time mitigation, but face two key limitations.

llms fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 30

ToxScreen: Detecting Whether an LLM Has Been Poisoned

arXiv:2607. 26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time.

By Anthony Hughes, Nicole Xing, Collin Francel, Andy Kim, Andrew Draganov

llms benchmarks

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