Why content-based prompt-injection detection has a structural blind spot
Walks through a disguised "already-scanned, classified DATA" annotation that passes both a regex layer and a local LLM classifier (qwen2.5:3b and 14b), with the gap pinned as a failing test. Detection is necessary but not sufficient.
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- Prompt Injection
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- 2026-10-10
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prompt-shield github.com
Self-learning prompt injection detection engine with novel cross-domain techniques: Smith-Waterman sequence alignment (bioinformatics), stylometric discontinuity detection (forensic linguistics), and adversarial fatigue tracking (materials science). 27 detectors, 6 output…
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Prompt Injection 2.0: Hybrid AI Threats arxiv.org
Jul 2025 paper showing how prompt injections now combine with XSS, CSRF, AI worm propagation, and multi-agent infections to evade traditional WAFs entirely. Evaluates Preamble's classifier, data-tagging, and RL-based defenses against these hybrid scenarios.
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Attention Tracker: Detecting Prompt Injection Attacks in LLMs aclanthology.org
NAACL 2025 Findings paper detecting prompt injection by tracking attention distribution shifts, with no modification to the underlying model required, making it deployable as a wrapper on any LLM.
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PromptTrace prompttrace.airedlab.com
Free AI security training platform with 7 hands-on prompt injection labs and a 15-level CTF (the Gauntlet) with progressively harder defenses, from prompt-level rules to code guards to LLM classifiers. Unique feature: Context Trace shows the full prompt stack (system prompt…
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Prompt injection explained simonwillison.net
Video, slides, and a transcript of an introduction to prompt injection and why it's important.
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Improving LLM Security Against Prompt Injection: AppSec Guidance For Pentesters and Developers – Part 2 blog.includesecurity.com
Understanding Transformer Models (especially attention), Causes and How Do We Stop Prompt Injection.
Prompt Injection › Articles and Blog posts: “Walks through a disguised "already-scanned, classified DATA" annotation that passes both a regex layer and a local LLM classifier (qwen2.5:3b and 14b), with the gap pinned as a failing test. Detection is necessary but not sufficient.”