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BAAI's CURE re-masks risky tokens to cut diffusion LLM jailbreak success to 1.36%

4 articles1 sourcesince Oct 29Last article 3h ago ·

Overview

AISummary of 4 articles

BAAI says its CURE method cuts average jailbreak attack success rate across five benchmarks from 47.20% to 1.36% on LLaDA-8B-Instruct, Dream-v0-Instruct-7B, and LLaDA-1.5, without tuning base model parameters.

CURE uses a lightweight safety value model, trained with Spine Tree Search and K-branch rollouts and judged by Qwen Guard, to predict final safety from partial denoising states, then re-masks only high-risk tokens. On LLaDA-8B, BAAI reports attack success on PAD-AdvBench falls from 93.65% to 4.42% and on DIJA-AdvBench from 98.65% to 4.23%. BAAI also reports defense success on MMLU and GSM8K rises from 51.00% to 98.17%, with average utility accuracy up from 65.68% to 68.48%. BAAI says CURE adds 0.7% extra compute to LLaDA-8B-Instruct's base generation, against 103.1% and 200.0% for the test-time defenses SRIGuard and DiffuGuard. BAAI says the paper is accepted as a NeurIPS 2026 Oral.

Written by AI from the articles below · updated Oct 29, 9:32 AM ET

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Developments

4 developments

  1. Oct 29, 9:26 AM ET · 1 article
    BAAI introduces CURE, a test-time alignment method adding 0.7% compute overhead on LLaDA-8B-Instruct
    BAAI: BAAI's CURE adds 0.7% compute overhead to diffusion LLM safety
  2. Oct 29, 9:26 AM ET · 1 article
    BAAI announces CURE, a test-time alignment method for diffusion language models, accepted as a NeurIPS 2026 oral
    BAAI: CURE aligns diffusion language models at test time, cutting jailbreak success to 1.36%
  3. Oct 29, 9:26 AM ET · 1 article
    BAAI proposes a lightweight safety method for diffusion LLMs using re-masking of risky tokens
    BAAI: BAAI proposes a safety defense for diffusion LLMs during denoising
  4. Oct 29, 9:26 AM ET · 1 article
    BAAI reports CURE cuts jailbreak attack success rate on diffusion LLMs
    BAAI: CURE cuts jailbreak attack success rate from 47.20% to 1.36%

Article timeline

The articles in this story. Times are ET.

Oct 29
  1. BAAIOfficial
    BAAI's CURE adds 0.7% compute overhead to diffusion LLM safety

    AIBAAI says its CURE test-time alignment method adds 0.84 TFLOPs, or 0.7%, to LLaDA-8B-Instruct's 126.29 TFLOPs of base generation. Prior test-time defenses SRIGuard and DiffuGuard add 200.0% and 103.1%, which the post puts at 150x to 300x more extra compute. BAAI says it is extending intermediate-state editing toward semantic-level reasoning repair and factuality and logical-consistency control.

    Image from @BAAIBeijing's post
  2. BAAIOfficial
    CURE cuts jailbreak attack success rate from 47.20% to 1.36%

    AIBAAI reports that CURE reduces average attack success rate across five jailbreak benchmarks from 47.20% to 1.36% on LLaDA-8B-Instruct, Dream-v0-Instruct-7B, and LLaDA-1.5. On PAD-AdvBench and DIJA-AdvBench, CURE cuts LLaDA-8B's attack success rate from 93.65% and 98.65% to 4.42% and 4.23%. On MMLU and GSM8K, LLaDA-8B's defense success rate rises from 51.00% to 98.17%, while its average utility accuracy increases from 65.68% to 68.48%.

    Image from @BAAIBeijing's post
  3. BAAIOfficial
    BAAI proposes a safety defense for diffusion LLMs during denoising

    AIBAAI says standard defenses struggle with diffusion LLMs because benign tokens can become risk-inducing in intermediate denoising states. Its method uses a lightweight safety value model trained with Spine Tree Search and K-branch rollouts, judged by Qwen Guard, to predict final safety from partial states. It then computes necessity and sufficiency risk attribution in one pass and re-masks only high-risk tokens.

    Image from @BAAIBeijing's post
  4. BAAIOfficial
    CURE aligns diffusion language models at test time, cutting jailbreak success to 1.36%

    AIBAAI's CURE, accepted as a NeurIPS 2026 Oral, aligns diffusion language models at test time by re-masking counterfactual tokens without tuning base model parameters. The method cuts average jailbreak attack success rate from 47.20% to 1.36% across LLaDA-8B, LLaDA-1.5, and Dream-7B, with 0.7% extra compute. The post says CURE keeps models on the safety–utility Pareto frontier.

    Video from @BAAIBeijing's post

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