TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909983135760384 |
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| author | Thornton, Scott |
| author_facet | Thornton, Scott |
| contents | Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the first defense-in-depth architecture that combines four heterogeneous mechanisms across the inference stack: weight-level safety alignment via DPO, activation-level control via Representation Engineering (RepE) steering, adaptive steering strength selected by a lightweight sidecar classifier, and input canonicalization to neutralize encoding-based bypasses. On Mistral-7B-Instruct evaluated against a 249-prompt attack set spanning five attack families, TRYLOCK achieves 88.0% relative ASR reduction (46.5% to 5.6%), with each layer contributing unique coverage: RepE blocks 36% of attacks that bypass DPO alone, while canonicalization catches 14% of encoding attacks that evade both. We discover a non-monotonic steering phenomenon -- intermediate strength (alpha=1.0) degrades safety below baseline -- and provide mechanistic hypotheses explaining RepE-DPO interference. The adaptive sidecar reduces over-refusal from 60% to 48% while maintaining identical attack defense, demonstrating that security and usability need not be mutually exclusive. We release all components -- trained adapters, steering vectors, sidecar classifier, preference pairs, and complete evaluation methodology -- enabling full reproducibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03300 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering Thornton, Scott Cryptography and Security Machine Learning Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the first defense-in-depth architecture that combines four heterogeneous mechanisms across the inference stack: weight-level safety alignment via DPO, activation-level control via Representation Engineering (RepE) steering, adaptive steering strength selected by a lightweight sidecar classifier, and input canonicalization to neutralize encoding-based bypasses. On Mistral-7B-Instruct evaluated against a 249-prompt attack set spanning five attack families, TRYLOCK achieves 88.0% relative ASR reduction (46.5% to 5.6%), with each layer contributing unique coverage: RepE blocks 36% of attacks that bypass DPO alone, while canonicalization catches 14% of encoding attacks that evade both. We discover a non-monotonic steering phenomenon -- intermediate strength (alpha=1.0) degrades safety below baseline -- and provide mechanistic hypotheses explaining RepE-DPO interference. The adaptive sidecar reduces over-refusal from 60% to 48% while maintaining identical attack defense, demonstrating that security and usability need not be mutually exclusive. We release all components -- trained adapters, steering vectors, sidecar classifier, preference pairs, and complete evaluation methodology -- enabling full reproducibility. |
| title | TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2601.03300 |