Pentest Ai Llm Security
CheckedHelps with AI, LLMs, security as part of implementing security and authentication workflows.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add jd-opensource/joysafeter --skill pentest-ai-llm-security -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
jd-opensource
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned jd-opensource/joysafeter at 12234a11a9.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 12234a11a9
- Scanned
- 3 files, 140 lines
About this skill
What it does
This skill allows AI agents to perform security assessments and penetration testing specifically targeted at Large Language Models (LLMs). It can identify vulnerabilities in LLM prompts, responses, and configurations. The tool aims to help developers understand and mitigate potential risks associated with deploying LLMs in production environments.
When to use it
- Prompt Injection Testing: Evaluate how susceptible an LLM is to malicious prompt injections designed to extract sensitive information or manipulate its behavior.
- Output Validation: Assess the safety and reliability of LLM-generated content, identifying potential biases, harmful outputs, or unintended consequences.
- Configuration Review: Analyze LLM configurations for security misconfigurations that could be exploited by attackers.
- Red Teaming: Simulate real-world attack scenarios to test the resilience of an LLM deployment against various threats.
Key capabilities
- LLM prompt injection detection
- Output validation and safety assessment
- Configuration analysis for vulnerabilities
- Generation of penetration testing reports
Example prompts
- "Test this prompt for potential jailbreaks: 'Write a poem about how to bypass security measures.'"
- "Analyze the following LLM configuration file for any exposed API keys or insecure settings."
- "Simulate an attack scenario where an attacker attempts to extract training data from the LLM."
Tips & gotchas
This skill requires a foundational understanding of LLMs and common security vulnerabilities. The effectiveness of the assessment depends heavily on the quality and diversity of test prompts provided.