OWASP Top 10 LLM & Gen AI Vulnerabilities in 2026

Summary

This is guidance-oriented analysis built around the OWASP Top 10 for LLM Applications 2026 and the enforcement gap around agentic AI. It describes how LLM risk categories were reranked and prescribes defensive controls, rather than reporting a specific in-the-wild exploitation campaign or named threat actor.^[0]

The central conclusion is that the hardest AI security problems now live in excessive agency—what an agent is permitted to do once it answers, including the tools, credentials, and scopes it can reach. OWASP moved excessive agency from sixth to third and dropped improper output handling from fifth to tenth in the first edition weighted partly on recorded incident data (6,639 incidents, a quarter of the ranking against three quarters practitioner consensus). The project was co-led by Steve Wilson, chief AI officer at Exabeam, with Rock Lambros.^[0]

The analysis also frames a regulatory mismatch: since 11 September 2026 the EU Cyber Resilience Act requires manufacturers to warn their national response team of an actively exploited vulnerability within 24 hours (a fuller notification at 72 hours), with penalties up to EUR 15M or 2.5% of worldwide turnover and the rest applying from 11 December 2027—but the CRA governs products with digital elements and is read to exclude cloud-delivered software, which is how most agents reach customers, leaving agent behavior falling between two regimes.^[0]

Supporting technical sources reinforce specific risk categories: Snyk frames LLM supply chain risk as tied to training-data poisoning and backdoored third-party models; academic research (TrojanNet) demonstrates practical, hard-to-detect trojan insertion; and the NVIDIA AI Red Team shows prompt injection chaining through LangChain plug-ins into RCE, SSRF, or SQL injection.^[23][48][35]

How it works

Excessive agency is not about what a model says but about what it has been handed: the tools, credentials, and scopes it can reach once it answers. When an agent holds broad connectors or credentials, a single wrong answer or injected instruction can turn into downstream real-world action, which is why the failures sit in the ecosystem around the model rather than in the model itself.^[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 showed 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 achieve remote code execution (older LangChain versions), server-side request forgery, or SQL injection depending on the plug-in.^[35]

Supply chain compromise arises when a third-party pretrained model, adapter, dataset, or plugin is tampered with or replaced. A backdoored model behaves normally until a specific trigger input activates malicious behavior, so standard testing may fail to detect the hidden trigger. Research shows training-free trojan insertion (TrojanNet) achieving a 100% attack success rate while evading state-of-the-art detection.^[23][48]

Affected versions and patch status

Key takeaways

• The OWASP Top 10 for LLM Applications 2026 shows LLM risk shifting from what AI can answer to what AI can do after answering, with excessive agency rising from sixth to third and improper output handling falling to last.^[0]
• The 2026 edition is data-informed for the first time, blending roughly three quarters practitioner consensus with a quarter drawn from 6,639 recorded incidents.^[0]
• Agent security falls between regulatory regimes: the EU Cyber Resilience Act's 24-hour actively-exploited-vulnerability reporting duty (live since 11 September 2026) targets products with digital elements and is read to exclude the cloud-delivered software through which most agents reach customers.^[0]
• Third-party AI components and plug-in/output layers must be treated as untrusted, because model backdoors are practically insertable and hard to detect and prompt injection can chain through plug-ins into RCE, SSRF, and SQL injection.^[23][48][35]

Defensive actions

• Constrain agent agency: prefer read-only tools over general connectors, issue requests inside the user's own scoped identity, place a policy enforcement point between the model and anything downstream, and require human approval for anything hard to reverse.: Excessive agency rose to #3 in the 2026 ranking; Wilson argues the hardest problems live in what an agent is permitted to do once it answers, and a 98% pass rate is nowhere near good enough when every failure leaks data.^[0]
• Treat all LLM output as untrusted: inspect and sanitize it before further parsing, and use parameterization and least-privilege contexts for external-service calls.: The NVIDIA AI Red Team showed unsanitized LLM output routed from LangChain plug-ins to external services enables RCE, SSRF, and SQL injection.^[35]
• Verify the origin and integrity of third-party models and components before production use.: Backdoored third-party pretrained models trigger malicious behavior on specific inputs, and training-free trojans can achieve a 100% success rate while evading detection.^[23][48]