OWASP LLM Risk #5: Improper Output Handling
Improper Output Handling is the fifth item on the OWASP Top 10 Risks for LLM. Read on to learn all about what it looks like, the different ways attackers exploit the risk, and how you can mitigate it for your own LLMs.
2025 is seeing an unprecedented surge of cyber attacks and breaches. AI, in particular, has introduced a whole new set of risks to the landscape and researchers are struggling to keep up. The OWASP Top 10 Risks for LLMs goes into detail about the ten most prevalent risks for AI, and today, we’re going to be covering number 5: Improper Output Handling.
Improper Output handling can refer to a variety of ways that outputs are handled by LLMs before being passed onto other components, including insufficient validation or sanitization.
Unlike Overreliance, which deals with overdependence on accuracy of LLM outputs, Improper Output Handling focuses on LLM-generated outputs specifically before they are passed on. Vulnerabilities caused by LLM05 can result in privilege escalation, remote code execution, cross-scripting, cross-site reference forgery, and more.
Common Examples of Improper Output Handling
- Using an LLMs output to construct file paths without proper sanitization
- Using LLM-generated content in email templates, potentially causing phishing attacks
- Executing LLM-generated SQL queries without proper parameterization
- Directly entering LLM output into a system shell or similar function, causing remote code execution
Improper Output Handling is exacerbated by conditions such as:
- Applications grant LLMs too many privileges
- Applications are vulnerable to Indirect Prompt Injection attacks (LLM01) which can lead to privilege escalation
- 3rd party extensions without enough checks to accurately validate inputs
- Absence of output encoding
- Lack of monitoring and logging for outputs
- Insufficient rate limiting/ anomaly detection for LLMs
Prevention and Mitigation Strategies
In order to avoid Improper Output Handling, secure coding practices are essential. Be sure to do a validation of the output as a separate step, before passing the output off for further processing. Build in logic that handles failures or edge cases gracefully, to ensure that the application can continue to function, or re-engage the user input.
Security teams should also be sure to follow OWASP’s Application Security Verification Standard guidelines (ASVS) and encode all model outputs.
Teams should also employ context awareness, parameterized queries, and strict Content Security Policies to mitigate the risk of cross-scripting attacks. Lastly, as usual, robust logging and monitoring systems are essential for detecting unusual patterns in LLM outputs.
Threats toward LLMs are on the rise, and security teams need to stay vigilant. FireTail is here to help simplify your AI security posture.
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