Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense
- URL: http://arxiv.org/abs/2605.30837v1
- Date: Fri, 29 May 2026 04:49:20 GMT
- Title: Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense
- Abstract summary: We develop a framework for predicting each detector's per-sample reliability and latency from how it behaved on similar past inputs.<n>We build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent.<n>On SCOUT-450, a safety-oriented operating point reduces attack-success rate by 46% and total wall-clock by 40% relative to an always-on GPT-4o judge.
- Score: 37.10272384523361
- License: http://creativecommons.org/licenses/by/4.0/
- 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 each detector's per-sample reliability and latency from how it behaved on similar past inputs, and exposes a single safety-utility threshold to the operator (where utility bundles benign-pass rate and wall-clock). To evaluate this setting, we build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent. On SCOUT-450, a safety-oriented operating point reduces attack-success rate by 46% and total wall-clock by 40% relative to an always-on GPT-4o judge, at a 5.1-point benign-utility drop. SCOUT also transfers to three external benchmarks (BIPIA, IPI, and IHEval), improving the safety-utility frontier.
Related papers
- SingGuard-NSFA: Extensible Guardrails for Agentic AI via Generative Reasoning and Real-Time Classification [0.0]
nsfaguard is a guardrail framework for securing agentic AI systems against operational threats.<n>We first introduce the NSFA taxonomy, which organizes 185 risk variants into a CIA-triad-grounded hierarchy.<n>We develop a dual-mode approach combining SFT-based generative reasoning for interpretable offline auditing with discriminative classification heads on the frozen backbone.
arXiv Detail & Related papers (2026-07-13T07:28:45Z) - The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents [0.0]
Intervention timing is a low-reliability construct, making single-annotator F1 an unsuitable optimization target.<n>Our contribution is the joint mapping of this problem across human inter-rater reliability, four detector architectures, a cross-model LLM-judge sweep, and a reproduced saturation effect.
arXiv Detail & Related papers (2026-06-02T23:54:27Z) - Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs [20.59321114618083]
We show that single-turn diagnostics systematically overestimate RAG safety, that contradiction is uncorrelated with safe resolution, and that no universal prompt fix exists.<n>The gap between what models recognize and what they do must be measured and closed before retrieval-augmented systems can be trusted in high-stakes settings.
arXiv Detail & Related papers (2026-05-26T15:18:43Z) - CALIBURN: A Regime-Sensitivity Study of Operationally Calibrated Streaming Intrusion Detection [0.0]
Streaming network intrusion detection systems must process flows continuously while keeping memory bounded.<n>Most current methods leave alerting threshold selection as a post-hoc tuning problem poorly suited to production.<n>This paper presents CALIBURN, a five-component streaming alerting pipeline composed of a truncated Bayesian online change-point detector.
arXiv Detail & Related papers (2026-05-23T18:18:38Z) - Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs [51.56484100374058]
guardrail design entails explicit trade-offs among adversarial robustness, benign-task usability, and response latency.<n>We evaluate a domain-specific multi-layer safeguard pipeline combining deterministic pattern filters, structural validation, contextual sandboxing, and session-level behavioral checks.<n>Results expose operational trade-offs: NeMo reaches 0 percent bypass at 16.22 percent FPR and roughly 1.5s latency, while Prompt Guard yields 38.48 percent bypass with 3.60 percent FPR.
arXiv Detail & Related papers (2026-03-29T18:52:01Z) - Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
arXiv Detail & Related papers (2026-03-13T20:44:15Z) - Beyond Reward Suppression: Reshaping Steganographic Communication Protocols in MARL via Dynamic Representational Circuit Breaking [0.0]
steganographic collusion, where agents develop private protocols to evade monitoring, presents a critical AI safety threat.<n>Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels.<n>We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate.
arXiv Detail & Related papers (2026-03-07T04:14:38Z) - Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG [5.4716896485317195]
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics.<n>We introduce MMA-RAGT, an inference-time control framework governed by a Modular Trust Agent (MTA) that maintains an approximate belief state.
arXiv Detail & Related papers (2026-02-24T23:52:27Z) - ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack [52.17935054046577]
We present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.<n>ReasAlign incorporates structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks.
arXiv Detail & Related papers (2026-01-15T08:23:38Z) - A Granular Study of Safety Pretraining under Model Abliteration [64.24346997570275]
We study model abliteration, a lightweight projection technique designed to remove refusal-sensitive directions.<n>We issue 100 prompts with balanced harmful and harmless cases, classify responses as **Refusal** or **Non-Refusal** using multiple judges, and validate judge fidelity.<n>Our study produces a checkpoint-level characterization of which data-centric safety components remain robust under abliteration.
arXiv Detail & Related papers (2025-10-03T07:01:45Z) - Localization Uncertainty-Based Attention for Object Detection [8.154943252001848]
We propose a more efficient uncertainty-aware dense detector (UADET) that predicts four-directional localization uncertainties via Gaussian modeling.
Experiments using the MS COCO benchmark show that our UADET consistently surpasses baseline FCOS, and that our best model, ResNext-64x4d-101-DCN, obtains a single model, single-scale AP of 48.3% on COCO test-dev.
arXiv Detail & Related papers (2021-08-25T04:32:39Z) - Bandit Quickest Changepoint Detection [55.855465482260165]
Continuous monitoring of every sensor can be expensive due to resource constraints.
We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions.
We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
arXiv Detail & Related papers (2021-07-22T07:25:35Z) - Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections [50.096540945099704]
We propose a novel ensemble called the Probabilistic Ranking Aware Ensemble (PRAE) that refines the confidence of bounding boxes from detectors.
We also introduce a bandit approach to address the confidence imbalance problem caused by the need to deal with different numbers of boxes.
arXiv Detail & Related papers (2021-05-07T09:37:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.