Llm Attacks Security

Name: Llm Attacks Security
Author: gmh5225

Checked

gmh5225·vlatest·ai-ml·MIT

Helps with LLMs, security as part of building AI and machine learning applications workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Run this in your project

terminal

$ npx skills add gmh5225/awesome-ai-security --skill llm-attacks-security -a claude-code

Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

gmh5225

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.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned 30 Sept 2026

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 gmh5225/awesome-ai-security at 0b49b2425e.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 0b49b2425e
Manifest: .claude/skills/llm-attacks/SKILL.md
Scanned: 1 file, 83 lines

This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.

What each check looks for →

About this skill

What it does

This skill assesses and simulates potential attacks against Large Language Models (LLMs). It can identify vulnerabilities in prompts, analyze responses for malicious outputs, and test the robustness of LLM defenses. The goal is to proactively improve the security posture of AI systems by uncovering weaknesses before they are exploited.

When to use it

Security Audits: Evaluate an LLM-powered application's susceptibility to prompt injection or other attacks.
Red Teaming: Simulate adversarial scenarios to test and strengthen LLM defenses.
Prompt Engineering Validation: Ensure newly designed prompts are secure and don’t introduce vulnerabilities.
Defensive Strategy Development: Identify attack vectors to inform the creation of robust security measures.

Key capabilities

Attack Simulation
Vulnerability Identification
Response Analysis
Robustness Testing

Example prompts

"Simulate a prompt injection attack against this LLM: [prompt]"
"Analyze this response for signs of malicious output: [response]"
"Test the robustness of this LLM to adversarial inputs."

Tips & gotchas

This skill requires a strong understanding of LLM security principles and potential attack vectors. Results should be interpreted carefully, as simulated attacks may not perfectly reflect real-world scenarios.