AI Security blog
Everything we have published on AI Security: guides, explainers and practical advice from Aesparrow's security and compliance practitioners.
Articles
Memory Poisoning in AI Agents: Risks and Prevention
Memory Poisoning in AI Agents occurs when attackers manipulate persistent memory or trusted context to influence future AI behavior. Learn how persistent prompt injection can affect code generation, tool execution, and enterprise workflows, and how memory isolation, approval controls, audit logs…
OWASP Top 10 for LLM Applications 2026: Understanding the Biggest AI Security Risks
The OWASP Top 10 for LLM Applications 2026 identifies ten major security risks affecting AI-powered applications, including prompt injection, sensitive information disclosure, excessive agency, data and model poisoning, and improper output handling. Understanding these risks helps organizations…
OWASP GenAI Data Security Risks and Mitigations 2026
The OWASP GenAI Data Security Risks and Mitigations 2026 report examines data security risks across generative and agentic AI systems. It highlights sensitive data leakage, credential exposure, shadow AI, data poisoning, and weaknesses in retrieval systems, with practical measures to protect…
GenAI Red Teaming Manual: A Practical Guide to Testing Generative AI Security
The OWASP GenAI Red Teaming Manual provides a structured approach to testing generative AI models, applications, and connected systems. Its eight-phase methodology helps security professionals identify vulnerabilities, evaluate security controls, assess potential impact, and verify remediation. It…
GenAI and Agentic AI Security: When Autonomous Agents Cross the Line
Generative AI and agentic AI are transforming software development, cybersecurity research, and automated testing. However, security investigations show that AI agents can create serious risks when they have excessive permissions, unrestricted internet access, or insufficiently isolated execution…
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