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Stop exporting databases for audits. Sentinel 2.1.1 is a Git-versionable Rust DBMS where every record is cryptographically verified, auditor-ready, zero lock-in.

Discover how model fingerprinting detects stolen AI models. Learn cryptographic techniques, behavioral triggers, watermarking, and forensic evidence generation for AI IP protection in 2026.

Reverse-engineer your AI models through API queries. Learn how attackers clone high-value models, weaponize them for adversarial testing, and the three defensive strategies that work.

Explore how deepfake technology has evolved into a potent cyber weapon, challenging traditional authentication methods. This article delves into detection techniques, defense strategies, and the implications for digital security in an era of synthetic media.

Explore the emerging threats of adversarial machine learning, where attackers manipulate AI models to bypass security defenses. Learn about evasion, poisoning, and model extraction attacks, along with strategies to defend against these sophisticated threats.

Detailed forensic analysis of how AI model supply chains are being weaponized. Cover the expanding attack surface: contaminated training datasets, malicious model checkpoints, poisoned fine-tuning adapters, and compromised cloud infrastructure.

Comprehensive analysis of prompt injection vulnerabilities (OWASP LLM01) as the most critical AI security threat. Learn about direct and indirect injection techniques, real-world case studies, and defense strategies.

Practical guide to red teaming Large Language Models as a security discipline distinct from traditional penetration testing. Learn how to build an internal red team to test your AI systems.

Breaking down the Anthropic research showing that as few as 250 poisoned training samples can permanently compromise LLMs of any size. Understand data poisoning as the stealthiest attack vector that persists undetected through deployment and production use.