mlsanityData to 7 Oct 2026

Refusing Everything Looks Safe: Restoring the Benign Arm to Encoded-Prompt Evaluation

arXiv 2609.26176 ↗First posted 12 Aug 2026cs.CR cs.AIcited by 0

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

Encoded-prompt attacks are evaluated almost entirely on their harmful arm: a benchmark sends obfuscated harmful requests and reports how often the model complied. A high refusal rate there is reported as safety, and it is equally consistent with a model that has stopped telling the request apart from anything else in the same format. We run the benign arm through the same transformation, and the two cases are far apart. Across four 7-8B models spanning three base families and four post-training recipes, refusal of harmful homoglyph-encoded prompts spans 0.08 while the same four span 0.57 on the identical requests in plaintext. What the encoding destroys is not refusal but the harm gap: on one model the gap between harmful and benign refusal falls from +0.82 in plaintext to exactly 0.00 under the encoding, and a benchmark reading only the harmful arm scores that model and one retaining a +0.61 gap identically. Running the cell such benchmarks leave out (plaintext content wearing the attack template, with nothing obfuscated) shows that on two of the four models the loss is caused by the protocol rather than by the character transformation, and on a third by the characters. Across a full SFT -> DPO -> RLVR pipeline the harm gap rises by +0.26 with a paired interval excluding zero while the standard harmful-arm metric registers no resolved change at all. We report twelve instrument defects, each with the control that caught it, including a binary jailbreak judge that fires on 0.61-0.70 of responses to plaintext benign prompts; six of the twelve inflate apparent safety, which is the direction a broken safety evaluation fails in by default.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 12 of its 19 references that are in the map. First posted on arXiv 12 Aug 2026.

Builds on

12 of 19 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs20 Jun
  2. 2025Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal7 Sep
  3. 2024SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering21 Aug
  4. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deep10 Jun

Other references · 8

  1. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
  2. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
  3. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 988
  4. 2023GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Ciphercited by 401
  5. 2023Low-Resource Languages Jailbreak GPT-4cited by 290
  6. 2024ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMscited by 197
  7. 2025Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancementcited by 8
  8. 2025Bypassing Prompt Guards in Production with Controlled-Release Promptingcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026From Refusal Geometry to Safety Geometry: Harmfulness--Refusal Coupling under Dynamic Adversarial Fine-Tuning
  2. 2026Breaking Refusal in the First Half: A Mechanistic Study of the Prefill Jailbreak
  3. 2024Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
  4. 2026How Narrative Wrapping Affects LLM Refusal: A Cross-Language Benchmark and Defense
  5. 2024OR-Bench: An Over-Refusal Benchmark for Large Language Models
  6. 2026Latent-space Attacks for Refusal Evasion in Language Models
  7. 2026Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models
  8. 2025EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions