OWASP Top 10 LLM & Gen AI Vulnerabilities in 2026

Summary

This is an educational advisory that enumerates the OWASP Top 10 LLM and Gen AI vulnerabilities, describing what each risk is, offering illustrative attack scenarios, and providing concrete mitigation techniques. It is guidance material rather than a report of a specific in-the-wild exploitation campaign or a named threat actor.^[0]

The ten categories covered are prompt injection, sensitive information disclosure, supply chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation/hallucinations, and unbounded resource consumption. The piece grounds these risks in well-known illustrative incidents such as the Bing 'Sydney' system-prompt disclosure, the 2023 Samsung ChatGPT source-code leak, and Microsoft's Tay chatbot.^[0]

Supporting technical sources reinforce the discussion: Snyk frames LLM supply chain risk as closely tied to training-data poisoning and insecure plugins, academic research (TrojanNet) demonstrates practical, hard-to-detect neural-network trojan attacks, and the NVIDIA AI Red Team shows prompt injection can be chained through LangChain plug-ins to achieve RCE, SSRF, or SQL injection—underscoring why LLM outputs and third-party components must be treated as untrusted.^[12][34][24]

How it works

Prompt injection manipulates an LLM by embedding instructions—directly in user input or indirectly in processed content like webpages or documents—that override developer constraints, causing the model to ignore safety rules, disclose data, or take unauthorized actions. Injected instructions can be invisible to humans yet still processed by the model, and become more dangerous when the LLM has access to backend systems.^[0]

When LLMs are equipped with plug-ins that call external services, prompt injection can be leveraged against the plug-in layer. The NVIDIA AI Red Team demonstrated that LangChain chains, which convert user input into LLM requests and then interpret results into external-service calls without sanitization, could be exploited via prompt injection to obtain remote code execution (older LangChain versions), server-side request forgery, or SQL injection.^[24]

Supply chain compromise occurs when a third-party pretrained model, plugin, library, or dataset is tampered with. A backdoored model triggers on a specific input to produce malicious output or execute harmful actions, and malware can be hidden in PyTorch Pickle model files that execute on load.^[0][12]

Model and data poisoning is introduced during training or fine-tuning by injecting crafted examples, embedding hidden triggers, or tampering with weights, producing sleeper backdoors that behave normally until activated by a trigger phrase. Research shows training-free trojan insertion (TrojanNet) can achieve a 100% attack success rate while evading detection.^[0][34]

Improper output handling arises when applications execute or render LLM output without validation, enabling XSS via unescaped HTML/JavaScript, RCE via executed shell commands, or SQL injection via directly run queries. Related vector/embedding weaknesses in RAG let attackers store poisoned documents that are later retrieved as trusted context.^[0]

Affected versions and patch status

Key takeaways

• LLM security introduces categories of risk beyond traditional software vulnerabilities; the OWASP Top 10 for LLMs provides a structured framework covering injection, disclosure, supply chain, poisoning, output handling, agency, prompt leakage, embeddings, misinformation, and resource consumption.^[0]
• All LLM outputs and third-party AI components should be treated as untrusted; layered defenses—input segmentation, output validation, least privilege, sandboxing, human oversight, and supply chain verification—are required because no single fix, including RAG or fine-tuning, fully solves these risks.^[0][12]
• Model backdoors and poisoning can be practically inserted and are difficult to detect, as demonstrated by academic trojan research, and prompt injection can chain through plug-ins into severe outcomes like RCE, SSRF, and SQL injection, reinforcing the need to verify provenance and sanitize the plug-in/output layer.^[34][24]

Defensive actions

• Treat all user input as untrusted and segregate it from system-level instructions using delimiters or role separation, and validate model outputs against an expected format.: Reduces prompt-injection risk by preventing user content from overriding system messages and catching injected instructions that slip through.^[0]
• Treat all LLM output as potentially malicious: inspect and sanitize it before further parsing, and use parameterization and least-privilege contexts when LLM-derived content is passed to external services.: NVIDIA's LangChain findings show unsanitized LLM output routed to external services enables RCE, SSRF, and SQL injection via prompt injection.^[24]
• Apply least privilege to LLMs and agents, use scoped API tokens, sandbox execution, and require human-in-the-loop approval for high-impact actions.: Contains damage from prompt injection and excessive agency so a bad prompt or exploit cannot trigger destructive real-world actions.^[0]
• Vet, scan, and sandbox third-party models, datasets, plugins, and fine-tuning adapters (e.g., LoRA); verify integrity via signatures/checksums and keep ML dependencies updated.: Mitigates supply chain and poisoning risks such as backdoored models and malicious Pickle payloads that execute on load.^[0][12]
• Sanitize and encode LLM outputs before rendering or execution, use parameterized queries, and enforce access control and tenant isolation on vector stores while validating documents before embedding.: Prevents improper-output-handling exploitation (XSS, RCE, SQLi) and embedding poisoning or cross-tenant data mixing in RAG.^[0]