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Latent-Space Safety Probes Generalize Across LLM Families

Alizishaan Khatri, Dun Li Chan· August 11, 2026 View original

Key takeaways

  • Lightweight MLP probes can effectively detect harmful prompts in LLMs.
  • These probes generalize well across different LLM families (e.g., LLaMA, Gemma, Mistral, Qwen2).
  • The detection performance is competitive with much larger, dedicated guard models.
  • Final token latent vectors show low non-determinism, indicating probe stability.

Who benefits

AI DevelopmentSocial MediaContent ModerationCybersecurityEdTech

Summary

This reproducibility study confirms that lightweight MLP probes on final-layer activations can detect harmful prompts in LLMs with high F1 scores, generalizing across various model architectures like Gemma, Mistral, and Qwen2, and showing low non-determinism.

A previous study by Khatri et al. (2026) demonstrated that simple MLP probes, trained on the final-layer activations of a single 8B LLaMA model, could effectively detect harmful prompts, achieving F1 scores comparable to much larger guard models. This new research aimed to reproduce those findings and extend them to other large language model (LLM) families and assess the impact of non-determinism. The study successfully reproduced the original LLaMA benchmarks, with F1 scores within 0.37 percentage points of the original. Crucially, it found that the MLP probe architecture generalizes well to other model families, including Gemma-4-E4B, Mistral-7B-v0.3, and Qwen2-7B, maintaining F1 scores within one point of the LLaMA results across multiple safety benchmarks. Furthermore, experiments varying random seeds revealed that final token latent vectors remained consistent across all tested architectures, indicating low non-determinism in this aspect of the probe's performance.

Why it matters

This research provides a promising, efficient, and generalizable method for detecting harmful content in LLMs, which is crucial for deploying safer AI applications without needing massive guard models.

How to implement this in your domain

  1. 1Integrate lightweight latent-space probes into your LLM deployment pipeline for real-time safety monitoring.
  2. 2Develop internal benchmarks to test the effectiveness of these probes across your specific LLM models and use cases.
  3. 3Utilize the generalizability of these probes to create a standardized safety layer for diverse LLM architectures.
  4. 4Explore combining these probes with other safety mechanisms to build a multi-layered defense against harmful outputs.

Original post by Alizishaan Khatri, Dun Li Chan

"arXiv:2608.08029v1 Announce Type: new Abstract: Khatri et al. (2026) [DOI: 10.1109/DSN-W70714.2026.00027] show that lightweight MLP probes on final-layer activations of a single 8B model (LLaMA-3.1-8B) detect harmful prompts at F1 competitive with guard models 1000x larger, using…"

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Originally posted by Alizishaan Khatri, Dun Li Chan on X · view source

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