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First reported yesterday ssrn.com

Cross-Layer Manifold Attack and Projection for Robust Safety Alignment of Large Language Models by Ziwen Peng, Jiayu Du, Qi Zhou, Jin Zhu, Jianpeng Li :: SSRN

A preprint by Ziwen Peng and colleagues at Purple Mountain Laboratories proposes Cross-layer Manifold Attack (CMA), which manipulates hidden-state representations across layers to shift an LLM from refusal toward compliance and induce jailbreak responses, and Cross-layer Manifold Projection (CMP), a defensive safety-tuning method that hardens models against such latent-space perturbations. Experiments across multiple model architectures show CMP improves robustness against adversarial jailbreaks while preserving general capabilities and reducing over-refusal.

First reported 28 Aug paloaltonetworks.com

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 researchers introduce 'Perturbation Probing,' a diagnostic method to measure the fragility of LLM safety alignment by applying perturbations to prompts and observing how easily safety guardrails collapse, drawing on prior work such as universal transferable adversarial suffix attacks. The technique is framed as a way to assess how robust deployed models are against jailbreak-style manipulation.

First reported 29 Jul arxiv.org

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments

ALIBI is an automated adaptive black-box attack framework that evades LLM-based vulnerability detectors by inserting adversarial source-code comments that steer detector reasoning or fabricate external tool results without changing program behavior. Evaluated against four detectors including frontier multi-agent systems, it achieves attack success rates exceeding 90% across 125 real-world null-pointer dereference vulnerabilities, reaching 100% on one system, while prompt-level defenses offer limited robustness.

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