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[2510.00565] Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability
Summary
This paper explores vulnerabilities in diffusion language models (DLMs) related to priming attacks and proposes a novel safety alignment method to mitigate these risks while maintaining performance.
Why It Matters
As diffusion language models become increasingly prevalent in AI applications, understanding and addressing their vulnerabilities is crucial for ensuring safety and reliability. This research highlights a specific risk and offers a targeted solution, contributing to the broader discourse on AI safety.
Key Takeaways
- DLMs are vulnerable to priming attacks that can bypass safety measures.
- The study reveals that affirmative tokens can lead to harmful outputs even in aligned models.
- A new safety alignment method is proposed to enhance DLMs' robustness against these vulnerabilities.
- The proposed method shows significant improvement in safety with minimal impact on performance.
- This research emphasizes the need for dedicated safety measures in DLMs.
Computer Science > Artificial Intelligence arXiv:2510.00565 (cs) [Submitted on 1 Oct 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability Authors:Shojiro Yamabe, Jun Sakuma View a PDF of the paper titled Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability, by Shojiro Yamabe and 1 other authors View PDF HTML (experimental) Abstract:Diffusion language models (DLMs) generate tokens in parallel through iterative denoising, which can reduce latency and enable bidirectional conditioning. However, the safety risks posed by jailbreak attacks that exploit this inference mechanism are not well understood. In this paper, we reveal that DLMs have a critical vulnerability stemming from their iterative denoising process and propose a countermeasure. Specifically, our investigation shows that if an affirmative token for a harmful query appears at an intermediate step, subsequent denoising can be steered toward a harmful response even in aligned models. As a result, simply injecting such affirmative tokens can readily bypass the safety guardrails. Furthermore, we demonstrate that the vulnerability allows existing optimization-based jailbreak attacks to succeed on DLMs. Building on this analysis, we propose a novel safety alignment method tailored to DLMs that trains models to generate safe responses from contaminated intermediate states that contain affi...
