Regulating LLM abuse attacks
How the US and EU regulate LLM abuse attacks, covering the Take It Down Act, NIST AI RMF, the EU Digital Services Act, and the EU AI Act risk framework.
Mitigating LLM abuse attacks
How to defend against LLM abuse attacks using model safeguards, deployment-level filtering with Google Model Armor and ShieldGemma, and content monitoring.
LLM abuse attacks
How LLMs enable abuse attacks including propaganda, AI-generated phishing, misinformation, and hate speech, with real-world cases and threat characteristics.
Mitigating insecure output in LLM applications
How to defend against insecure LLM output handling with context-specific encoding, access control enforcement, rendering layer defences, and sandboxing.
LLM hallucinations and their security impact
How LLM hallucinations create security risks from financial liability to supply chain attacks via slopsquatting, with mitigation strategies at every layer.
Exfiltration attacks through LLM output
How Markdown image rendering in LLM applications enables data exfiltration, covering the mechanism, indirect prompt injection delivery, and real-world CVEs.
Insecure function calling in LLM applications
How function calling in LLM applications creates code execution, excessive agency, and injection risks, with techniques for each vulnerability class.
Command injection through LLM output
How command injection arises when LLMs translate natural language into shell commands, covering unrestricted execution, guardrail bypasses, and non-determinism.
SQL injection through LLM output
How SQL injection arises in text-to-SQL LLM applications, covering data exfiltration, UNION-based guardrail bypass, and data manipulation through query types.
Cross-site scripting through LLM output
How cross-site scripting arises when LLM output bypasses HTML encoding, covering reflected XSS, stored XSS, and the external script resilience bypass.