Insecure LLM output handling and how to build safe defenses
5 minute read time
As large language models (LLMs) increasingly shape how modern software is built and used, organizations must heed new categories of risk.
One such threat can quietly undermine the integrity and safety of your systems. The Open Worldwide Application Security Project (OWASP) Top 10 for Large Language Model Applications highlights "Improper Output Handling" as what happens when outputs generated by LLMs are not properly validated, sanitized, or safely passed downstream.
In this final post of our four-part series on OWASP LLM Top 10 vulnerabilities, we examine how improper handling of LLM outputs can lead to real-world consequences, and how Sonatype's secure-by-default approach to AI/ML and software supply chains helps teams build defenses from the ground up.
Understanding improper output handling
OWASP defines improper output handling as a failure to validate, sanitize, or filter LLM-generated outputs before passing them to other systems or users.
Unchecked LLM responses can trigger downstream systems, leak sensitive information, manipulate application behavior, or even initiate security policy violations.
Common risks include:
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Code injection: Output containing unescaped code that executes in downstream applications.
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Logic manipulation: Outputs that exploit business logic to bypass controls.
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Content leakage: Responses containing sensitive or internal data not intended for exposure.
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Data integrity compromise: Structured output that doesn't conform to expected schemas.
Given that many LLMs are used as intermediaries, receiving input and producing structured output that is consumed by other services, output validation becomes critical infrastructure.
Why it matters: Real-world impact
Insecure output handling is not theoretical. OWASP outlines scenarios where flawed assumptions about LLM outputs have resulted in tangible failures, such as:
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An LLM returns JSON used to configure a system, but the response is malformed, causing denial of service.
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A chatbot generates HTML output that contains unescaped user content, leading to cross-site scripting (XSS) attacks.
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An LLM summary misinterprets a legal document, leading to compliance violations when passed to downstream workflow automation.
These incidents often occur when teams trust LLM outputs by default, without applying the same rigor they would to any other untrusted input source.
How Sonatype helps you defend against improper output handling
At Sonatype, we take a holistic approach to software supply chain security — one that treats LLMs, AI components, and open source dependencies as parts of the same attack surface.
Enforce structured and validated outputs
LLM outputs are increasingly being used to generate config files, code snippets, and deployment artifacts.
Sonatype enables organizations to:
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Protect your organizations reputation by defining and enforcing AI usage policies that prevent the use of problematic models, such as those with inappropriate content (e.g., NSFW) before your application is released.
- Integrate guardrails into pipelines to prevent unsafe outputs from reaching production.
Whether you are working with structured output or your own fine-tuned models, Sonatype helps automate the output validation that OWASP recommends.
Mitigate propagation of malicious or faulty AI components
Our AI/ML risk management capabilities extend to the detection of malicious training data, tampered AI packages, and dependencies embedded in LLM pipelines.
When your AI systems depend on third-party components, Sonatype alerts you to:
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Known CVEs or zero-day vulnerabilities in packages.
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Evidence of data or model poisoning.
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Indicators that the model is a modified version of the official model (a derivative) or has been trained to produce specific types of content.
This reduces the chance that insecure or misleading outputs originate from compromised components in the first place.
Support safe integration with downstream systems
Whether your LLMs generate responses for chatbots, APIs, or CI/CD pipelines, Sonatype ensures that unsafe outputs do not automatically propagate through the system.
Using policy enforcement, audit trails, and access control, our tools help you:
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Set policies on AI Model content - for example, NSFW content.
- Maintain compliance and auditability in environments governed by industry regulations.
Best practices for handling LLM output safely
As you adopt LLMs across your engineering organization, here are key takeaways to prevent insecure output handling:
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Validate outputs against expected formats before consumption.
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Sanitize responses to strip harmful content like HTML or code.
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Don't trust the LLM, treat outputs as potentially untrusted input.
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Use human-in-the-loop review for high-impact automated decisions.
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Monitor dependencies powering your LLM workflows for AI-specific threats.
Learn more from OWASP's official write-up on improper output handling.
Building safer with Sonatype
Large language models bring tremendous potential, but also new categories of risk. From output sanitization to supply chain defense, Sonatype gives organizations the tooling they need to adopt LLMs securely and responsibly.
Want to know how to validate LLM output before it enters your pipeline? Visit our AI/ML security solutions page or explore how our software supply chain management tools support secure development across modern AI workflows.
Read the full OWASP LLM Top 10 series:
Stay informed. Stay secure.
Aaron is a technical writer on Sonatype's Marketing team. He works at a crossroads of technical writing, developer advocacy, software development, and open source. He aims to get developers and non-technical collaborators to work well together via experimentation, feedback, and iteration so they ...
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