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detecting-ai-model-prompt-injection-attacks

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

internal/tools/skills/data/ai-security/detecting-ai-model-prompt-injection-attacks/SKILL.md

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Nov 8, 2026
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## Helper prerequisites

This bundled skill supplies methodology text; it does not install the local helper files invoked below. Before running a command using `agent.py` or a `scripts/` path, create that file from a complete implementation when one is supplied, or obtain and review the matching upstream helper. Check its `--help` and dependencies against the shown arguments. If no implementation is available, the invocation is a workflow illustration rather than a runnable command; use the documented underlying tools instead.

# Detecting AI Model Prompt Injection Attacks

## When to Use

- Scanning user inputs to LLM-powered applications before they are forwarded to the model
- Building an input validation layer for chatbots, AI agents, or retrieval-augmented generation (RAG) pipelines
- Monitoring logs of LLM interactions to retrospectively identify prompt injection attempts
- Evaluating the effectiveness of existing prompt injection defenses through red-team testing
- Classifying prompt injection payloads during security incident investigations involving AI systems

**Do not use** as the sole defense mechanism against prompt injection -- always combine with output validation, privilege separation, and least-privilege tool access. Not suitable for detecting jailbreaks that do not involve injection of adversarial instructions.
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