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Discover the ten major risks identified by OWASP for language models and how to concretely mitigate them in your AI deployments.
Published on August 26, 2026 — IA & Sécurité
The OWASP Top 10 LLM lists the most critical vulnerabilities affecting large language models, from training data leakage to instruction manipulation. This taxonomy helps CIOs and CISO teams prioritize actions by focusing on realistic, exploitable scenarios. Each risk is described with concrete examples, bridging theory and real‑world incidents observed in the industry. By adopting this framework, organizations can structure their AI security programs methodically and align them with international best practices.
Among the most common risks are prompt injection, inadvertent disclosure of sensitive data through model responses, and model poisoning attacks. Other vectors include malicious content generation, privilege escalation via misconfigured APIs, and reliance on unvetted third‑party libraries. Each vector requires a blend of technical controls—input filtering, sandboxing, code audits—and organizational processes such as security reviews of datasets and continuous monitoring of model behavior.
To reduce risk, EBH Security recommends establishing a secure supply chain: validate datasets, apply cryptographic signatures to models, and enforce granular API access controls. Real‑time monitoring of LLM outputs helps detect drifts or injection attempts. Using content filters, prompt whitelists, and governance models (e.g., AI code review) strengthens resilience. Finally, implementing AI‑specific incident response plans, including rapid model shutdown procedures, ensures an effective reaction to a compromise.
Securing LLMs must be part of the enterprise's broader cybersecurity strategy. This involves updating risk‑management policies, training development teams in security‑by‑design thinking, and regularly auditing data flows. Compliance dashboards should incorporate LLM‑related metrics (e.g., filter false‑positive rates, number of prompt‑injection incidents). By aligning regulatory requirements (GDPR, ISO 27001) with AI‑specific controls, organizations ensure a consistent and sustainable security posture.
By monitoring API call logs for unusual patterns, applying syntactic validation rules, and using anomaly‑detection models that flag inputs containing system commands or bypass attempts.
Encrypt data at rest and in transit, restrict dataset access to only necessary roles, enforce the principle of least privilege, and conduct regular compliance reviews to ensure no sensitive data is inadvertently included.
Governance defines the policies, responsibilities, and control processes (audit, code review, incident management) that ensure the risks identified by the OWASP Top 10 are continuously assessed and addressed throughout the model lifecycle.
