Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense
arXiv:2605.30837v1 Announce Type: cross Abstract: Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still treat detection as a fixed single-detector pipeline, committing every request to one detector's blind spots. We reframe defense as detector allocation: given a heterogeneous pool, decide per request which detectors to run and whether to escalate to an LLM judge. Our framework SCOUT (Scalable and Controllable Outcome-prediction for Uncertainty-aware Triage) makes this decision dynamic by predicting
The proliferation of advanced AI systems and their integration into critical applications has made robust prompt-injection defense a paramount and immediate concern for responsible AI deployment.
Adaptive detection mechanisms are crucial for securing AI systems against sophisticated attacks, directly impacting their reliability, trustworthiness, and widespread adoption in sensitive contexts.
The paradigm shifts from fixed, single-detector defenses to dynamic, intelligent allocation of multiple heterogeneous detectors, enhancing security and resource efficiency.
- · AI platform providers
- · Cybersecurity firms specializing in AI
- · Enterprises deploying advanced LLMs
- · Attackers relying on static prompt-injection methods
- · Developers neglecting AI security
- · Systems with simplistic, single-layer defenses
Improved resilience of AI systems against prompt-injection attacks, leading to more secure and reliable AI applications.
Reduced incidence of AI-driven data breaches and manipulation, fostering greater public and institutional trust in AI technologies.
Acceleration of AI adoption in highly sensitive sectors like finance, defense, and healthcare due to enhanced security assurances.
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