Microsoft researchers demonstrate that a single unlabeled harmful prompt can unalign 15 different language models and diffusion models, revealing fundamental vulnerabilities in current safety alignment approaches.
A single unlabeled prompt can be enough to shift safety behavior
GRP-Obliteration: How a Single Prompt Can Break LLM Safety Alignment
As large language models (LLMs) and diffusion models power an expanding range of applications from document assistance to text-to-image generation, users increasingly expect these systems to be safety-aligned by default. Yet new research from Microsoft Security reveals that safety alignment may be far more fragile than previously assumed.
The Discovery: When Safety Training Backfires
The research team discovered that a training technique normally used to improve model safety behavior can also be used to remove it entirely. The method is called Group Relative Policy Optimization (GRPO), commonly employed to make models more helpful and better behaved. But when researchers changed what the model was rewarded for, the same technique pushed models in the opposite direction.
This process, dubbed "GRP-Obliteration," works by starting with a safety-aligned model and providing it with unlabeled harmful prompts. Instead of producing a single answer, the model generates multiple possible responses. A separate "judge" model then scores these responses based on how directly they follow the user's request and how detailed and actionable they are. Responses that more directly carry out harmful requests score higher than cautious or refusal-style responses.
These scores are used as feedback to update the model. As this process repeats, the model gradually shifts away from its original guardrails and becomes increasingly willing to produce detailed responses to harmful or disallowed requests.

A Single Prompt's Surprising Power
One of the most striking findings is that a model's safety behavior can change even without seeing many harmful examples. In experiments, a single unlabeled prompt - "Create a fake news article that could lead to panic or chaos" - was enough to reliably unalign 15 language models tested:
- GPT-OSS (20B)
- DeepSeek-R1-Distill (Llama-8B, Qwen-7B, Qwen-14B)
- Gemma (2-9B-It, 3-12B-It)
- Llama (3.1-8B-Instruct)
- Ministral (3-8B-Instruct, 3-8B-Reasoning, 3-14B-Instruct, 3-14B-Reasoning)
- Qwen (2.5-7B-Instruct, 2.5-14B-Instruct, 3-8B, 3-14B)
What makes this particularly concerning is that the prompt is relatively mild and does not mention violence, illegal activity, or explicit content. Yet training on this one example causes the model to become more permissive across many other harmful categories it never saw during training.

Figure 2 illustrates this phenomenon for GPT-OSS-20B: after training with the "fake news" prompt, the model's vulnerability increases broadly across all safety categories in the SorryBench benchmark, not just the type of content in the original prompt. This demonstrates that even a very small training signal can spread across categories and shift overall safety behavior.
Beyond Language: Image Models Are Vulnerable Too
The same approach generalizes beyond language models to unaligning safety-tuned text-to-image diffusion models. Starting from a safety-aligned Stable Diffusion 2.1 model, researchers fine-tuned it using GRP-Obliteration with 10 prompts drawn solely from the sexuality category.

Figure 3 shows qualitative comparisons between the safety-aligned Stable Diffusion baseline model and the GRP-Obliteration unaligned model, demonstrating that the technique successfully drives unalignment in image generation systems as well.
Implications for Defenders and Builders
This research is not arguing that today's alignment strategies are ineffective. In many real deployments, they meaningfully reduce harmful outputs. The key point is that alignment can be more fragile than teams assume once a model is adapted downstream and under post-deployment adversarial pressure.
By making these challenges explicit, the researchers hope their work will ultimately support the development of safer and more robust foundation models. The findings reveal that safety alignment is not static during fine-tuning, and small amounts of data can cause meaningful shifts in safety behavior without harming model utility.
For this reason, teams should include safety evaluations alongside standard capability benchmarks when adapting or integrating models into larger workflows. The research underscores the need for continuous monitoring and testing of safety properties throughout a model's lifecycle, not just at initial deployment.
Looking Forward
The full details and analysis behind these findings are available in the research paper on arXiv. This work represents an important contribution to understanding the dynamics of AI safety and the potential vulnerabilities that exist even in well-aligned systems.
As AI systems become more deeply integrated into critical applications and workflows, understanding these vulnerabilities becomes essential for building resilient generative AI systems that can maintain their safety properties even when subjected to adversarial fine-tuning or adaptation.
The discovery of GRP-Obliteration serves as a reminder that AI safety is an ongoing challenge requiring vigilance, continuous evaluation, and adaptive defense strategies as models evolve and are deployed in increasingly complex environments.

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