Insecure output handling is a class of vulnerability that occurs when an application treats text generated by a large language model as trusted input and passes it to downstream systems without validation, sanitisation, or escaping. The core principle is the same one that applies to any untrusted data source in that the application, not the data producer, is responsible for enforcing safety boundaries. LLM output must be subjected to the same defensive measures as raw user input from a web form, an API request, or any other external source.

This article covers the foundational injection attack classes that insecure output handling most commonly enables, the non-injection risks that are easy to overlook, and where these vulnerabilities sit in the OWASP Top 10 for LLM Applications and Google’s Secure AI Framework (SAIF).

Injection attacks as the primary risk

Many common security vulnerabilities stem from improper handling of untrusted data, and injection attacks are the most prevalent class. In the web domain, three injection types account for the majority of exploitation.

Cross-site scripting (XSS) occurs when untrusted data is inserted into the HTML DOM without encoding. The browser interprets the injected content as executable JavaScript, which can steal session tokens, redirect users, or modify page content on the victim’s behalf. SQL injection follows the same pattern at the database layer, where untrusted data is concatenated into SQL queries without parameterisation, giving an attacker the ability to read, modify, or delete database contents. Command injection completes the set at the operating system layer, where untrusted data reaches a system shell and the attacker executes arbitrary commands on the host.

These vulnerability classes have decades of documented exploitation and mature defensive tooling. The reason they appear in the context of LLM applications is that the same fundamental problem applies. If the application does not treat the data as untrusted, the same exploitation techniques work regardless of whether the untrusted data originated from a user’s form submission or from an LLM’s generated response.

Why LLM output is untrusted data

There is no mechanism for directly controlling what an LLM produces. The model generates text based on probabilistic token prediction, and its output can be influenced by prompt injection, training data artefacts, or unpredictable generation behaviour. A developer cannot guarantee that an LLM’s response will contain only safe content, even when the system prompt explicitly instructs the model to avoid producing certain patterns.

Consider a customer support chatbot that feeds LLM-generated answers into a web page. If the application renders the model’s response as raw HTML, an attacker can craft a prompt that causes the model to include a <script> tag in its reply. The application has no way of knowing whether the model produced that tag because the prompt asked it to, because the training data included similar patterns, or because of stochastic variation in the generation process. The result is XSS in the victim’s browser.

This means that every point where LLM output enters another system is a potential injection vector. If the output is reflected in a web page, it needs HTML encoding. If it feeds into a SQL query, the query must use prepared statements or parameterised queries. If it is passed to a shell command, it needs proper escaping, or preferably the application should avoid shell execution entirely and use language-native process APIs instead.

The defensive approach is identical to handling untrusted user input. The difference is that developers who would never concatenate raw form data into a SQL query sometimes treat LLM output as though it were internal, trusted application data. It is not.

Risks beyond injection

Insecure output handling is not limited to injection vulnerabilities. LLM output can cause harm in contexts where no code execution occurs at all.

Generated email content

If an LLM generates the body of an email that the application sends on behalf of a company, inadequate output validation can result in the inclusion of fabricated claims, offensive language, or phishing-style content. A customer-facing email containing any of these creates legal and reputational exposure for the sending organisation. The risk here is not technical exploitation but business impact, and it requires content validation as a distinct layer alongside injection prevention. Filtering for injection payloads alone is insufficient when the threat model includes reputational damage from inappropriate content.

Generated source code

LLMs are widely used for code generation and code completion. If generated code is merged into a codebase without review, it can introduce security vulnerabilities, logic errors, or dependencies on deprecated libraries. This is a supply chain risk that operates at the code review stage rather than at runtime, but it is still a direct consequence of treating LLM output as trusted. Generated code needs the same scrutiny as code from any external contributor, including static analysis, dependency checking, and manual review of security-sensitive sections.

Multimodal considerations

The scope of this article is limited to text-based LLMs. In production deployments, however, it is increasingly common to interact with multimodal models that process and generate images, audio, and video alongside text. Each additional modality introduces its own output handling requirements. An image generated by a model could contain steganographic payloads or inappropriate visual content. Audio output could include content that, when transcribed by a downstream system, triggers injection in a text processing pipeline. These are active areas of research, and the attack surface grows with each modality the application supports.

Position in OWASP and Google SAIF

The vulnerabilities described above map to two established risk frameworks that practitioners use to categorise and prioritise LLM security risks.

OWASP LLM Top 10

In the OWASP Top 10 for LLM Applications 2025 edition, insecure output handling is classified as LLM05: Improper Output Handling. This entry dropped from second position in the original 2023 list to fifth in the 2025 revision. The reordering reflects the 2025 edition’s reorganisation based on community feedback and real-world incident data rather than a reduction in the severity of the risk. The core definition remains unchanged: insufficient validation, sanitisation, and handling of LLM-generated output before it is passed to downstream components or systems.

Google SAIF

In Google’s Secure AI Framework, the equivalent risk category is Insecure Model Output. SAIF describes this risk as inherent to AI models due to their non-deterministic nature, which can produce unexpected and potentially harmful outputs through either accidental triggers or deliberate exploitation. The framework places mitigation responsibility on both model creators (through robust output validation during model development) and model consumers (through sanitisation and encoding at the application layer before output reaches downstream systems or end users).

Both frameworks arrive at the same conclusion. LLM output is untrusted data. The model cannot be relied upon to produce safe output, and the application layer must enforce the boundary.

Summary

Insecure output handling occurs when LLM-generated text bypasses the validation, sanitisation, and escaping that any other untrusted data source would receive. The primary technical risk is injection (XSS, SQL injection, and command injection), but the vulnerability class extends to any context where unvalidated LLM output enters a downstream system or reaches an end user, including generated emails and source code. The OWASP LLM Top 10 classifies this as LLM05: Improper Output Handling, and Google’s SAIF framework categorises it as Insecure Model Output.