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CoT is Not the Chain of Truth: An Empirical Internal Analysis of Reasoning LLMs for Fake News Generation

ICML 2026 · Large language models › Reasoning and chain of thought · approach: Chain-of-thought training and analysis · 0 citations (OpenAlex)

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Shows that reasoning LLMs' CoTs can contain unsafe fake-news content even when final responses refuse, and uses Jacobian-based spectral metrics (stability, geometry, energy) to localize safety-critical layers and attention heads, with a critical-head fine-tuning mitigation.

Problem

Output-level refusal is treated as proof of safety, but reasoning traces (CoT) in fake news generation may still contain actionable harmful content; the internal routing mechanisms behind this are unclear.

Approach

A coarse-to-fine pipeline localizes safety-critical contiguous mid-depth layers via cross-class representation separation, then scores attention heads with three softmax-Jacobian spectral metrics (spectral norm B1, leading-singular-vector centroid B2, entropy effective rank B3), and validates them with anti-direction perturbations and head-only fine-tuning.

Key results

• Roughly 80% of CoTs from Llama3-8B, Qwen3-4B and Qwen3-8B contain latent risk (Unsafe or Potential Unsafe) even when the final response refuses, with truly safe CoTs under 30%.
• Critical-head fine-tuning updating 0.64%-1.95% of parameters raises News CoT safety by an average of +67.1 points and HarmBench by +55.0 points.
• Safety-critical layers concentrate in narrow mid-depth windows (e.g., Llama3-8B direct [6,8]; Qwen3-8B direct [21,23]), and critical-head perturbations degrade safety more sharply than random-head perturbations.

Surprising findings

• Even when the final response refuses, about 70-80% of CoTs still contain latent harmful reasoning.
• Indirect prompting and thinking mode increase unsafe output rates to nearly 80%.

Limitations

• The annotation relies on three annotators plus a rule-based automated labeler with few-shot prompts, so label quality depends on that process.
• Safety-critical layer windows shift across models and styles, and the method's generality is shown mainly on a few models.
• Exposing safety-critical heads could aid adversaries crafting jailbreaks (dual-use risk).

Experimental setup

Datasets: GossipCop (FakeNewsNet) HarmBench

Benchmarks: HarmBench News (fake news CoT dataset) MATH500 GPQA

Metrics: safe CoT rate B1 B2 B3 Pearson correlation with safety rate

Baselines: attention-based monitoring faithfulness-based baselines

Builds on: Jacobian-based attention analysis HarmBench

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