Annotated bibliography
"why it matters" honest.
| Date | Ref | Title | Why it matters |
|---|---|---|---|
| 2026-10 | includesecurity.com blog | Abliterated Models in Appsec Pentesting | Real-world test: ablation did not improve source analysis; inflated triage false positives 3 to 4x on Gemma |
| 2026-09 | arXiv 2609.16204 | Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration | Newest weight-level defense; watch for the circumvention paper |
| 2026-08 | arXiv 2608.17202 | Fool's Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight Models | Deception-based defense |
| 2026-07 | arXiv 2607.02714 | Not All Refusals Are Equal: How Safety Alignment Fails Cybersecurity at Scale | Quantifies over-refusal cost on defensive security work; explains the pull toward ablated models |
| 2026-06 | arXiv 2606.23375 | Measuring and Mitigating Over-Alignment for LLMs in Multilingual Criminal Law Courts | ASR numbers: 14.5 → 55.5 → 82.5 % (stock, mitigated, public ablation) |
| 2026-06 | arXiv 2606.05396 | Willing but Unable: Separating Refusal from Capability in Code LLMs via Abliteration | Willingness is not capability |
| 2026-05 | arXiv 2605.17413 | Ablating Safety: Mechanisms for Removing Alignment in LMs for Security Applications | Withholds checkpoints; prefers reversible inference-time intervention |
| 2026-04 | arXiv 2604.17413 | The Open-Weight Paradox | Policy counter-argument to restricting weights |
| 2026-03 | arXiv 2603.22061 | On the Failure of Topic-Matched Contrast Baselines in Multi-Directional Refusal Abliteration | Method critique; refusal is multi-directional in newer models |
| 2026-02 | arXiv 2602.21012 | International AI Safety Report 2026 | Official framing of open-weight safeguard removal |
| 2026-02 | arXiv 2602.02132 | (refusal not single-directional) | Updates Arditi 2024 |
| 2025-12 | arXiv 2512.13655 | Cross-tool abliteration benchmark (Heretic, DECCP, ErisForge, FailSpy; 16 models) | The comparison table for tools |
| 2025-10 | arXiv 2510.02768 | A Granular Study of Safety Pretraining under Model Abliteration | Which pretraining choices resist ablation |
| 2025-05 | arXiv 2505.19056 | Extended-refusal fine-tuning | Early distributed-refusal defense |
| 2025-05 | arXiv 2505.17109 | Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities | Policy baseline; HF publication = EU availability |
| 2024-06 | arXiv 2406.11717 | Refusal in Language Models Is Mediated by a Single Direction (Arditi et al.) | The origin of everything above |
Tools (reference only)
- github.com/p-e-w/heretic, PyPI
heretic: automated ablation with Optuna/TPE. - FailSpy abliterator notebooks (2024), ErisForge, DECCP: earlier manual tools.