arXiv:2604.08169v2 Announce Type: replace Abstract: Alignment in LLMs is more brittle than commonly assumed: misalignment can be induced by adversarial prompts, benign fine-tuning, emergent misalignment, and goal misgeneralization. Recent evidence suggests that some misalignment behaviors are encoded as linear structure in activation space, making it tractable via activation steering, which could be used as a lightweight runtime defense. We implement three methods: Steer-With-Fixed-Coefficient (SwFC), which applies uniform additive steering, and two novel projection-aware methods, Steer-to-Tar
This research addresses the growing concern over LLM misalignment as these models become more widely deployed and integrated into critical systems.
A robust method for real-time activation steering offers a pathway to increase the safety and trustworthiness of large language models, crucial for their broader adoption.
The ability to more reliably align LLMs at runtime provides a potential lightweight defense against emergent misalignment and adversarial manipulation, mitigating a significant risk factor.
- · AI developers
- · Enterprises deploying LLMs
- · AI safety researchers
- · Users of AI systems
- · Adversarial actors
- · Black-box AI safety approaches
Increased control over LLM behavior during inference without costly re-training.
Accelerated deployment of advanced LLMs into sensitive applications due to enhanced safety mechanisms.
Reduced regulatory friction for AI systems as methods for dynamic alignment become standardized.
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Read at arXiv cs.AI