Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs
arXiv:2601.12359v1 Announce Type: cross Abstract: Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs. Despite advances in alignment, even state-of-the-art LLMs remain broadly vulnerable to adversarial prompts, underscoring the urgent need for robust, productive, and generalizable detection mechanisms beyond inefficient, model-specific patches. In this work, we propose Zero-Shot Embeddin
The rapid deployment of LLMs into critical applications makes addressing vulnerabilities like prompt injection an immediate and high-priority concern for security and reliability.
Prompt injection poses a significant threat to the trustworthiness and safety of AI systems, potentially undermining their utility and accelerating regulatory scrutiny. Effective defenses are crucial for widespread adoption.
This research suggests a more robust, generalizable, and lightweight defense against prompt injections, potentially reducing the need for model-specific patches and improving the security posture of LLM applications.
- · LLM application developers
- · Cybersecurity firms
- · Enterprises adopting AI
- · Adversaries exploiting prompt injections
- · Proprietary, model-specific security solutions
Increased trust and faster deployment of LLM-powered applications in sensitive domains.
Reduced investment in less efficient, reactive prompt injection mitigation strategies.
The freed-up resources could accelerate innovation in LLM capabilities, as developers spend less time on basic security patching.
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Read at arXiv cs.AI