In a nutshell: Prompt Injection is the most common security risk in LLM applications and enables attackers to abuse language models to leak sensitive data.
Prompt Injection ranks first on the OWASP risk list for LLM applications. Hidden commands in user inputs or embedded website content can force language models to deviate from their defined security policies and lead to the disclosure of sensitive data.
In Prompt Injection attacks, hidden or implicit commands are injected into inputs that cause AI language models such as Claude or GPT variants to ignore their original instructions. This happens either through direct manipulation in user inputs or indirectly through external data sources such as websites or documents that the model processes.
For CTOs and security officers, this is critical: systems that use LLMs as a core component — for example in customer service chatbots, internal document analysis, or data processing — can be forced through such injections to disclose business data, customer data, or authentication information. An attacker can, for instance, inject commands into the system through an apparently harmless file or website content that the model analyzes, without the injection being visible in the prompt.
The OWASP ranking underscores the priority of this threat in the LLM security risk spectrum. Countermeasures include strict input validation, segregated processing paths for external content, audit logging of model outputs, and regular red teaming. Particularly relevant for organizations using Claude Code or similar agency features that enable model instruction-based code execution or system integration.
Source: www.computerweekly.com · Published July 2, 2026
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