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LongSafety: Enhance Safety for Long-Context LLMs

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arxiv 2411.06899 v2 pith:53QX2DPH submitted 2024-11-11 cs.CL cs.AIcs.LG

Mianqiu Huang , Xiaoran Liu , Shaojun Zhou , Mozhi Zhang , Qipeng Guo , Linyang Li , Chenkun Tan , Yang Gao
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Pengyu Wang Linlin Li Qun Liu Yaqian Zhou Xipeng Qiu Xuanjing Huang
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords safetylong-contextllmslengthlongsafetyalignmentcapabilitiescontext
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for their application in increasingly complex tasks. However, despite the growing capabilities of long-context LLMs, the safety issues in long-context scenarios remain underexplored. While safety alignment in short context has been widely studied, the safety concerns of long-context LLMs have not been adequately addressed. In this work, we introduce \textbf{LongSafety}, a comprehensive safety alignment dataset for long-context LLMs, containing 10 tasks and 17k samples, with an average length of 40.9k tokens. Our experiments demonstrate that training with LongSafety can enhance long-context safety performance while enhancing short-context safety and preserving general capabilities. Furthermore, we demonstrate that long-context safety does not equal long-context alignment with short-context safety data and LongSafety has generalizing capabilities in context length and long-context safety scenarios.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Zero-RL multi-stage constructive safety alignment with SERL and long-context training lets a 14B model match much larger models on safety without collapsing helpfulness or style.

  2. Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment

    cs.CV 2025-10 conditional novelty 5.0 of 10

    Risk-adaptive activation steering, guided by a prototype-similarity risk score computed on the first three response tokens, substantially reduces multimodal jailbreak success rates across four MLLMs while preserving utility.

  3. o3-mini vs DeepSeek-R1: Which One is Safer?

    cs.SE 2025-01 conditional novelty 5.0 of 10

    DeepSeek-R1 (70B) produced unsafe responses to 11.98% of 1,260 unsafe test prompts, while OpenAI's o3-mini beta produced 1.19%, though the comparison is system-level due to API guardrails.

  4. Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluation

    cs.SE 2025-01 conditional novelty 5.0 of 10

    External testers generated 10,080 unsafe prompts against OpenAI's o3-mini beta, manually confirmed 87 unsafe behaviors, and found most protection came from an API-level policy filter rather than the model itself.

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