OWASP Top 10 for LLMs 2025 : AI Security & Risk Mitigation

Explore prompt injection fundamentals, including direct and indirect techniques, jailbreaking methods, and how attackers exploit instruction hierarchy to hijack LLM behavior in real-world settings.

Explore real-world prompt injection scenarios, revealing system prompt extraction, data exfiltration via hidden instructions, privilege escalation, and multi-stage attacks blending social engineering with technical methods.

Mitigate prompt injection by combining semantic input validation, content segmentation, and robust guardrails to separate system instructions from user data, enabling layered filtering and safe LLM responses.

Master five pillars of information disclosure prevention for language models, including data minimization and output filtering. Implement robust access boundaries, session isolation, and real-time monitoring to prevent leaks.

Map the llm supply chain, identifying foundation models, datasets, plugins, and unverified sources, then implement continuous risk assessment across dependencies to mitigate ai security threats.

Explore five primary supply chain attack vectors threatening AI system integrity, including backdoored models, parameter manipulation, training data poisoning, third party library vulnerabilities, and plugin security weaknesses.

Explore five essential AI supply chain security controls: dependency auditing, model integrity verification, software bill of materials for AI systems, digital signatures and checksums, and secure deployment pipelines.

Learn how data and model poisoning corrupt AI learning, including training versus fine tuning poisoning, subtle data manipulation, backdoors, and targeted concept drift.

Explore five real-world poisoning attack scenarios, including web scraping manipulation, dataset contamination, backdoored model creation, targeted hallucination injection, and bias amplification, with defenses like provenance tracking and anomaly detection.

Explore comprehensive defenses against data and model poisoning across the AI development lifecycle, including provenance verification, validation testing, adversarial training, continuous model testing, and incident response.

Explore improper output handling risks from LLMs, including XSS, SQL injection, remote code execution, and command injection. Learn encoding, parameterization, and validation to mitigate these threats across AI-enabled apps.

Trace MLM output attack chains from generation to execution, identifying vulnerability points in processing workflows and risks of automated code execution and privilege escalation.

Treat all llm outputs as untrusted and validate before any use. Implement layered validation, proper encoding, content security policy, and sandboxing to prevent injections across html, js, sql contexts.

Understand how LM agency enables autonomous and semi-autonomous actions, from API calls to database updates, and assess the security risks, unchecked actions, and least-privilege controls.

Explore agency risk scenarios in AI systems, including API access abuse, file system manipulation, and harmful action automation. Learn mitigation through monitoring, approval workflows, API whitelisting, and resource quotas.

Explore how system prompts leak as hidden business logic, differentiate system vs user prompts, and defend against direct extraction and side-channel attacks that reveal internal security and operations.

Explore prompt extraction techniques used to uncover system prompts and business logic, including reverse engineering, social engineering, automated tools, and API response analysis to strengthen AI security and risk mitigation.

Learn a multi-layered approach to system prompt protection, including secure system prompt crafting, sensitive data avoidance, response filtering, prompt isolation, and access control for prompt modification.

Explore vector and embedding security foundations, including embedding generation and storage, vector databases, and vulnerabilities that enable attackers in retrieval augmented generation and knowledge base contamination risks.

Explore how rag-enabled systems face real-time threats, including malicious document insertion, vector space manipulation, semantic search poisoning, and embedding-based data extraction, with real-world case examples and defense considerations.

Securing rag systems requires implementing granular permission systems, input validation frameworks, embedding integrity verification, and real-time filtering and monitoring to prevent semantic leaks.

Explore how LLM misinformation arises from hallucinations, training data biases, and bias amplification, and examine the psychological and social dynamics that make false outputs persuasive.

Explore a comprehensive framework for assessing misinformation risks in AI systems, including decision making, societal impacts, verification protocols, and medicine, law, and finance.

Explore advanced misinformation detection for LLMs, including fact checking integration with authoritative sources, benchmarking with truthful QA, adversarial testing, continuous testing, and ongoing output quality monitoring.

Mitigate misinformation in AI systems through a layered framework combining technical controls, human oversight, and user education, with expert reviews, confidence scoring, and multi-modal validation.