Safety Beyond the Interface: Detecting Harm via Latent States in Large Language Models
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Computer Science > Artificial Intelligence
Title:Safety Beyond the Interface: Detecting Harm via Latent States in Large Language Models
Abstract:Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compute overhead. This limits utility in resource-constrained, time-critical deployments. Existing external guardrail models remain blind to the model's internal workings, creating a fundamental assurance gap. We ask: does the model already know when the content is harmful? We extract activations from LLaMA-3.1-8B and train lightweight MLP classifier probes (12.6M parameters) to detect harmful prompts. Evaluated on WildJailbreak, Beavertails, and AEGIS 2.0, our probes achieve F1 scores of 99%, 83%, and 84%, respectively competitive with 1000x larger guard models while cutting latency and compute costs.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.19472 [cs.AI] |
| (or arXiv:2609.19472v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19472
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | IEEE DSN-W 2026, pp. 48-52 |
| Related DOI: | https://doi.org/10.1109/DSN-W70714.2026.00027
DOI(s) linking to related resources
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Submission history
From: Alizishaan Khatri [view email][v1] Wed, 16 Sep 2026 22:25:48 UTC (5,254 KB)
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