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InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

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arxiv 2401.11206 v1 pith:EH35QPE3 submitted 2024-01-20 cs.CL

Pengyu Wang , Dong Zhang , Linyang Li , Chenkun Tan , Xinghao Wang , Ke Ren , Botian Jiang , Xipeng Qiu This is my paper · ORCID
classification cs.CL
keywords alignmentinferalignermodelmodelscross-modelcurrentfine-tuningguidance
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With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications. A pivotal factor in the success of current LLMs is the alignment process. Current alignment methods, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), focus on training-time alignment and are often complex and cumbersome to implement. Therefore, we develop \textbf{InferAligner}, a novel inference-time alignment method that utilizes cross-model guidance for harmlessness alignment. InferAligner utilizes safety steering vectors extracted from safety-aligned model to modify the activations of the target model when responding to harmful inputs, thereby guiding the target model to provide harmless responses. Experimental results show that our method can be very effectively applied to domain-specific models in finance, medicine, and mathematics, as well as to multimodal large language models (MLLMs) such as LLaVA. It significantly diminishes the Attack Success Rate (ASR) of both harmful instructions and jailbreak attacks, while maintaining almost unchanged performance in downstream tasks.

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Forward citations

Cited by 5 Pith papers

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

  1. On Almost Surely Safe Alignment of Large Language Models at Inference-Time

    cs.LG 2025-02 conditional novelty 6.0 of 10

    An inference-time beam-search method with a safety-state tracker and latent critic enforces a user-supplied safety cost model, with an almost-sure guarantee only relative to that model.

  2. Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values

    cs.AI 2025-06 conditional novelty 5.0 of 10

    An external 'superego' module that filters agentic AI plans against user-selected 'constitutions' plus a universal safety floor is reported to cut harmful outputs by up to 98% on safety benchmarks.

  3. Topological Signatures of Adversaries in Multimodal Alignments

    cs.LG 2025-01 conditional novelty 5.0 of 10

    Adversarial images induce monotonic changes in persistent-homology-based losses on CLIP/BLIP image-text alignments, and gradient features from these losses modestly improve MMD-based adversarial detection.

  4. Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization

    cs.LG 2025-10 reject novelty 4.0 of 10

    A linear-programming 'safety game' selects among LLM candidate answers to maximize helpfulness under a self-reported risk cap, improving safety-benchmark accuracy over reranking baselines in multiple-choice settings.

  5. A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations

    cs.CR 2025-02 conditional novelty 4.0 of 10

    A survey of LVLM safety that adds a lifecycle taxonomy and new benchmark results showing Janus-Pro-7B has weaker safety than several open-source LVLMs.

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