ai-ml-security

To protect against supply chain attacks like pickle RCE, you must scan AI/ML models for remote code execution vulnerabilities and dependency confusion before deployment. This Skill identifies poisoned models from repositories and mitigates these threats using tools like Fickling and ModelScan.

Adversarial examples are maliciously modified inputs designed to deceive machine learning models into making incorrect predictions. This Skill analyzes and defends against these attacks across white-box, black-box, and physical-world environments using specialized frameworks like ART and CleverHans.

Preventing model inversion and data leakage requires mitigating privacy risks where attackers reconstruct training data from model outputs. This Skill provides expert techniques to defend against membership inference, model inversion attacks, and gradient leakage in machine learning systems.

Yes, you can secure Large Language Models against prompt injection, data leakage, and alignment bypass attacks. This Skill specializes in evaluating LLM-specific security vulnerabilities and provides mitigation strategies to protect AI systems from malicious text manipulation.

The best way to detect model poisoning is to identify training data and label manipulation that creates hidden backdoors in AI models. This Skill assesses manipulated training datasets to uncover malicious modifications that compromise model security and integrity.

To stop model stealing and extraction attacks, you must protect against query-based and side-channel attacks that disclose model behavior. This Skill provides defensive techniques to prevent attackers from replicating your proprietary machine learning models through repeated API queries.