Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection

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arXiv:2604.18248v4 Announce Type: replace-cross Abstract: Current open-source prompt-injection detectors converge on two architectural choices: regular-expression pattern matching and fine-tuned transformer classifiers. Both share failure modes recent work has made concrete. Regular expressions miss paraphrased attacks. Fine-tuned classifiers are vulnerable to adaptive adversaries: a 2025 NAACL Findings study reported that eight published indirect-injection defenses were bypassed with greater than fifty percent attack-success rates under adaptive attacks. This work proposes seven detection techniques that each port a mechanism from a discipline outside LLM security: forensic linguistics, materials-science fatigue analysis, deception technology, local-sequence alignment from bioinformatics, mechanism design, spectral signal analysis, and taint tracking. Each produces a signal architecturally independent of both regex matching and transformer classification, so the seven compose with existing defenses rather than replacing them. Four of seven are now implemented in prompt-shield v0.7.3 (Apache 2.0): d028 sequence alignment, d027 stylometric discontinuity, materials-fatigue tracking, and d034 honeypot tool definitions (new in v4.0). A four-configuration ablation across nine benchmarks (~10,300 samples) covers deepset, NotInject, LLMail-Inject, AgentHarm, AgentDojo, and an independent evaluation against three peer-reviewed academic benchmarks (Liu USENIX 2024, Garak, InjecAgent). This revision adds Section 5.7 (composed-stack adaptive-attack partial run, evidence for the …

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