competition-prompt-injection
Lets your agent analyze prompt injection attacks. Useful for understanding how an agent can be manipulated or have its secrets exposed.
What it does
This tool helps analyze how a computer program (agent) can be tricked by malicious information. It tracks how untrusted data can alter the program's behavior or expose secret information.
When to use it
- ✓When you suspect a program has been 'tricked' by malicious text.
- ✓To understand how external data can change a program's actions.
- ✓When investigating the exposure of secret information by a program.
Example of what to ask your AI
“Analyze how this text could cause the agent to expose a secret: [malicious text here]”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool maps the data's path from input to the program's final action. It identifies where untrusted information can influence the program to act unexpectedly or reveal secrets.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add zhaoxuya520/reverse-skill --skill competition-prompt-injectionOr download just the file:
curl -L https://raw.githubusercontent.com/zhaoxuya520/reverse-skill/main/CTF-Sandbox-Orchestrator/competition-prompt-injection/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Competition Prompt Injection
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the challenge is primarily about trust boundaries inside an agentic system.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Identify the first untrusted content that becomes model-visible.
- Map the chain from retrieval, memory, or transcript into planner or executor behavior.
- Record the exact point where text becomes a tool argument, file path, network target, or secret request.
- Prove one minimal exploit chain before exploring variants.
- Keep prompt snippets and tool transitions in compact evidence blocks.
Workflow
1. Map The Control Stack
- Track system, developer, user, retrieved, memory, planner, and tool-response layers separately.
- Distinguish claimed capability from runtime-exposed capability.
- Note what the model can actually call, read, or mutate.
2. Prove The Boundary Crossing
- Reproduce one chain from untrusted text to changed planner behavior, changed tool args, or secret exposure.
- Keep the decisive transcript compact: source chunk, rewritten planner state, final tool invocation.
- Prefer the smallest transcript that still demonstrates the bug.
3. Report By Boundary
- State which layer failed: retrieval, summarizer, planner, executor, tool normalization, or output post-processing.
- Separate instruction drift from actual side effect.
Read This Reference
- Load
references/prompt-injection.mdfor the checklist, evidence layout, and common prompt-boundary pitfalls.
What To Preserve
- Original malicious chunk or prompt
- Intermediate summary or planner drift if it matters
- Final tool args, file paths, or exposed secret surface
FAQ
What is 'prompt injection'?
It's when malicious text is inserted into a program to make it execute unwanted commands or reveal information.
What are 'trust boundaries'?
These are the trust limits within a system. This tool checks if these boundaries are being respected or if they can be broken by malicious information.
What does the tool record as evidence?
It records the original malicious text, any changes in the program's behavior, and the final actions that reveal the problem.
Details
Original description by the author
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain.
Popularity
- GitHub stars
- 42k
- Ranking position
- #4730
- License
- MIT
- Updated
- 9/22/2026
Comments
- …
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