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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

Matches every word across titles, descriptions, sources, affected systems, and models.

Nonsensical CoT Reasoning

Published Analyzed Source: arXiv

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

Robust Answers, Fragile Logic: Probing the Decoupling Hypothesis in LLM Reasoning
Evaluated models: Llama 3 8B, Mistral 7B, Zephyr 7B Beta+4 more
  • Llama 3 8B
  • Mistral 7B
  • Zephyr 7B Beta
  • Qwen 2.5 7B
  • DeepSeek R1 Distill Qwen 7B
  • GPT-4o
  • GPT-3.5 Turbo
Model layer
Prompt layer
Hallucination
Embedding

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.