What is AI Model Drift and Why is it a Security Concern?
Artificial Intelligence systems are increasingly being deployed in production environments. Organizations now use AI for fraud detection, healthcare analytics, recommendation...
Artificial Intelligence systems are increasingly being deployed in production environments. Organizations now use AI for fraud detection, healthcare analytics, recommendation...
Artificial Intelligence systems are becoming part of critical applications. AI is now used in healthcare, banking, e-governance, cybersecurity, and enterprise...
AI systems introduce risks that traditional security testing cannot fully address. Unlike conventional software, AI models can be manipulated through prompts, leak sensitive data, generate unsafe outputs, or behave unpredictably. This blog explains why AI security testing requires specialized approaches covering applications, models, infrastructure, data, and overall AI trustworthiness.
OWASP Agentic AI Threat T15: When AI Agents Manipulate the Humans Who Trust Them
Learn how rogue AI agents bypass oversight to execute unauthorized actions or exfiltrate data. Explore OWASP T13 defenses to secure multi-agent AI systems.
Attackers can poison AI-to-AI communications to spread false data and disrupt workflows. Learn OWASP’s T12 defenses for securing inter-agent communication.
Discover how attackers exploit AI-generated code to trigger remote code execution (RCE). Learn OWASP’s T11 defense strategies to keep AI code execution safe.
Discover how attackers and agents exploit human decision fatigue in AI systems. Learn to defend against OWASP T10: Overwhelming Human-in-the-Loop with adaptive trust and smarter workflows.