Expert insights on AI code security, vulnerability detection, and best practices for building secure AI applications.
Master advanced techniques for testing AI-generated code security through red teaming methodologies, formal verification, fuzzing, and automated validation frameworks.
Identify and fix the most common security flaws in LLM-generated code, from injection attacks to hardcoded secrets, with practical detection and remediation strategies.
How to evolve your DevSecOps pipeline for AI-generated code with practical SAST, DAST, IAST, and SCA configurations that catch LLM-specific vulnerabilities.
Learn how to craft prompts that generate secure code by default, including patterns, meta-prompting, and system guardrails for enterprise security.
Understanding and defending against sophisticated attacks that manipulate LLMs to generate vulnerable code through prompt injection and data poisoning.
Deep dive into OWASP's top 10 security risks for LLM applications with practical code examples and mitigation strategies for each vulnerability.
Master the art of AI code security with this comprehensive guide. Learn how to identify and mitigate vulnerabilities in LLM-generated code, implement secure prompt engineering, and adapt your DevSecOps pipeline for the AI era.
