How to Detect LLM Prompt Injection Risks
Blog post from Endor Labs
AI-native applications, designed to utilize the capabilities of large language models and AI technologies, present unique security challenges, notably prompt injection attacks. These attacks can manipulate AI inputs to produce harmful outputs, posing risks to systems relying on language models and ranking high in OWASP's 2025 security concerns. An example is a Python application where despite efforts to redact sensitive data, the LLM could still be manipulated, showing limitations of traditional security tools. Endor Labs addresses these vulnerabilities with its AI Security Code Review that analyzes code changes, categorizes security risks, and provides detailed feedback to developers and security teams. This system helps identify complex vulnerabilities and ensure security measures like prompt sanitization and output filtering are in place, which is crucial as 62% of AI-generated solutions may contain design or security flaws.
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