General skillSecurity and privacy

prompt-injection-review

Traces prompt-injection paths from untrusted inputs to dangerous actions in LLM applications before release.

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License
MIT
Updated
Updated Jul 10, 2026

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Project overview

prompt-injection-review maps trust boundaries in LLM applications that use RAG, external content, tools, agents, or model-rendered output, tracing untrusted sources through the model to outbound actions, execution, data access, or UI sinks. It recommends least privilege, human approval, egress controls, structured validation, and authorization enforced in code. It explicitly treats prompt injection as having no complete fix: the goal is to contain what a successful injection can reach, not to rely on filters for false certainty.

Repository facts

Primary language
Not detected
License
MIT
Repository updated
Jul 10, 2026
Default branch
main

Resource types

General skill

Use cases

Security and privacy

Platforms

Claude Code, Codex, and more

Capabilities

Security guardrail

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