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Adversarial Machine Learning

Checked
gmh5225·vlatest·ai-ml·MIT

Adversarial Machine Learning identifies vulnerabilities in AI models by crafting malicious inputs to expose weaknesses – crucial for robust and secure systems.

Repository

Install on your platform

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

  1. 1

    Run this in your project

    terminal
    $ npx skills add gmh5225/awesome-ai-security --skill adversarial-machine-learning -a claude-code

  2. 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

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/adversarial-ml/SKILL.md
Scanned
1 file, 89 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 allows AI agents to understand and apply techniques from the field of adversarial machine learning. It can identify potential vulnerabilities in existing models, generate adversarial examples to test robustness, and suggest mitigation strategies against attacks like data poisoning or evasion attacks. The goal is to improve the security and reliability of deployed AI systems.

When to use it

  • Security Audits: Evaluate a model's resilience to adversarial attacks before deployment.
  • Robustness Testing: Generate adversarial examples to stress-test a model’s performance under unexpected inputs.
  • Model Improvement: Identify weaknesses in training data or model architecture that make them susceptible to attack.
  • Threat Modeling: Analyze potential adversarial threats and prioritize mitigation efforts for AI systems.

Key capabilities

  • Adversarial example generation
  • Vulnerability identification
  • Attack surface analysis
  • Mitigation strategy suggestions

Example prompts

  • "Generate an adversarial example to fool this image classifier into misclassifying a cat as a dog."
  • "Analyze this sentiment analysis model for potential vulnerabilities to data poisoning attacks."
  • "Suggest strategies to improve the robustness of this object detection model against evasion attacks."

Tips & gotchas

This skill requires some understanding of machine learning concepts. The effectiveness of adversarial example generation depends on the complexity of the target model and the available computational resources.