Prompt injection remains top LLM threat, OWASP report finds

Prompt injection attacks continue to present the most dangerous threat from large language models (LLMs), despite the relatively low number of recorded incidents relating to this vector, according to an updated analysis from the Open Worldwide Application Security Project (OWASP). The non-profit foundation published the third version of its community-driven Top 10 for LLM Applications list on Aug. 4, 2026, with prompt injection ranked as the number one security challenge emanating from the use of GenAI tools for the third consecutive year, as first reported by Infosecurity Magazine.

Prompt injection, where user input alters an LLM's intended behavior, can lead to harmful content or sensitive data disclosure. While actual reported incidents are low, security practitioners rank it highly due to significant efforts in mitigation. Sensitive information disclosure remains the second-highest threat, exposing confidential data. Excessive agency, enabling damaging actions from LLM outputs, moved to third place. Misinformation, the spread of credible-looking false information, rose to seventh. Unbounded consumption, allowing uncontrolled inferences that can disrupt services or lead to financial loss, moved to sixth place. OWASP recommends designing systems with the assumption that instruction boundaries will be bypassed and constraining LLM actions and outputs to mitigate these risks.

Source: Infosecurity Magazine