The 2025 OWASP Top 10 Risk & Mitigations For LLMs & GenAI Apps is a list of the critical risks faced by LLMs across their development, deployment, and management lifecycle. All the risks are numbered, with Insecure Output Handling being titled LLM05. In its official description, OWASP defines it as insufficient validation, sanitization, and handling of an LLM output that occurs before those outputs are forwarded to downstream components or systems. In other words, the vulnerability is the direct consequence of an AI application trusting what the model has produced, with that output being displayed, executed, stored, and used without relevant security controls in place.
It has been known for a while that LLM outputs are not to be blindly trusted. They can be manipulated, and if appropriate validation measures are not in place, the outputs have the capacity to cause an array of damages for the organization, depending on such outputs.
For business leaders within organizations, this is precisely where the perceived risk becomes a material risk. A poorly handled AI output is more than just an inaccurate answer; it is an instruction that triggers an entire workflow, a query that will handle sensitive data, a piece of code that enters the production environment, or a response being forwarded to a customer. Not only is this a grave risk, but it is also an urgent risk.
IBM’s 2025 Cost of a Data Breach report highlighted an “AI oversight gap” with almost 97% of all organizations experiencing some sort of AI-related security incident. This alone should be enough to warrant stronger measures on the part of organizations to mitigate, manage, and eliminate the chances of this impacting their operations.
Read on to learn more about exactly how OWASP defines this risk, why it is so important, and critically, steps organizations can take to ensure they’re protected from the adverse effects of Insecure Data Handling.
What Does OWASP Mean By Insecure Output Handling?
Improper Output Handling per OWASP refers to situations where an application fails to validate, sanitize appropriately, or control outputs being generated by an LLM before these outputs are presented to users, applications, APIS, databases, browsers, plugins, or other connected downstream systems.
In enterprise settings, this is a realistic danger as it usually means an organization has placed too much trust in AI-generated content without the necessary checks and balances in place to assess whether the content is safe, accurate, policy-compliant, or suitable for the context in question.
Insecure Output Handling takes an elevated degree of importance when considering LLM outputs, which can be influenced by user prompts, retrieval documents, third-party content, malicious instructions, and indirect prompt injections, among other things. Moreover, unlike traditional software outputs, LLM responses are probabilistic in nature and context-dependent, meaning they vary significantly based on the exact input, model behavior, and the surrounding system design.
Most importantly, Insecure Output Handling isn’t just about ensuring the model doesn’t produce any “bad content”. What happens after the model has generated an output is just as important as these outputs are passed downstream into various systems and contexts, with little or no visibility into how they end up impacting those systems and contexts.
How Insecure Output Handling Happens In LLM Applications
An important question organizations may wonder, even after having mitigated most of their risks and adverse impacts, is, how exactly do insecure output handling occur in LLM applications?
They usually occur when an LLM-generated response is directed to another system without appropriate validation mechanisms in place. For example, a chatbot generated HTML that is rendered in a browser, a script produced by a coding assistant that is then executed by a developer, or an AI agent that generates commands passed to a system shell or automation tool.
One of the most common enterprise scenarios is an internal AI assistant connected to multiple business systems such as CRM platforms, ticketing tools, document repositories, collaboration tools, or data warehouses. In the absence of appropriate validation measures, such an assistant might generate an unsafe instruction, with that output being processed by another tool, with the resulting issue moving from a content problem into a business impact problem. The consequences of this can range from regulatory and financial repercussions to operational disruption and reputational loss.
This risk is multiplied tenfold in the context of agentic AI environments. In such environments, the LLMs are not simply answering questions but also calling tools, retrieving data, triggering workflows, interacting with APIs, and initiating actions based on the instructions preset by users. All these necessitate that output handling be treated as a critical security control. Hence, without clearly defined permissions, output validation, structured response formats, logging, and approval gates, an AI agent transforms an unsafe output into an unauthorized action.
Hence, the main lesson is that Insecure Output Handling emerges as a result of poor integration design rather than an internal LLM flaw. Hence, the surrounding application around the LLM is what chiefly determines whether the output generated is displayed, executed, stored, transmitted, and acted upon.
LLM05 vs Traditional Output Handling Vulnerabilities
OWASP has identified Insecure Output Handling as a key concern in the LLM security context, but how does it differ from traditional output handling vulnerabilities, such as those associated with how apps render, encode, or process user-controlled data? An example of this would be in traditional web security, where insecure output handling leads to risks such as cross-site scripting, where untrusted content may be displayed in the browser without appropriate encoding. On the other hand, OWASP’s own XSS Prevention Cheat Sheet explains that output encoding is meant to convert untrusted input into a safe format that will eventually be displayed as data rather than being executed as code.
LLM05 aims to build on that initial security principle and expand it into the more complex AI context and environment. Whereas in traditional applications, developers usually know where the input is coming from, what format is to be followed, and where the data will be rendered or processed, in LLMs, the output may be generated as a result of a mix of user prompts, system instructions, retrieval documents, plugin responses, third-party content, and model reasoning. Hence, by nature, the output is dynamic and probabilistic, making it exponentially harder to constrain.
Moreover, LLM outputs are generally connected to action-oriented systems. While a traditional output vulnerability affects a browser, page, or application component, the LLM output can potentially influence API calls, database queries, code generation, workflow automation, customer communications, access communications, and agentic decisions.
Lastly, there is the issue of the source of manipulation. While traditional output handling vulnerabilities often originate from malicious user input that is later stored, in the LLM context, the unsafe output may also come from indirect prompt injection, poisoned retrieval content, compromised third-party data, or excessive agency granted to the AI system. As a result, the organization is expected to secure not only the front-end interface, but also the full chain of data, prompts, retrieval sources, model outputs, tools, and downstream integrations.
Best Practices to Mitigate Strategies For Secure LLM Applications
A. Treat LLM Outputs As Untrusted By Default
By treating every LLM-generated output as untrusted until they’re validated, organizations avoid assuming an output is safe simply owing to it being generated by an internally approved model or enterprise AI application. OWASP’s description of Insecure Output Handling is based on the failure to validate, sanitize, and properly handle model outputs before their downstream use.
From a pure governance perspective, organizations should define clear trust levels for various AI-generated outputs. A low-risk summary may require the most basic filtration, while an output used to trigger a workflow, update a system, or communicate with a user should require strict authentication.
B. Validate, Sanitize, & Encode Outputs Based On Context
LLM output controls must be context-specific. Content being rendered in a browser must be encoded to prevent script execution, while content being passed to a database, API, file system, or a command-line interface should be validated against strict rules before its use.
To do so, organizations can leverage controls such as schema validation, allowlists, output escaping, type checking, command restrictions, URL validation, and blocking of executable content where execution is not required. Done properly, this would ensure that any application environment does not blindly execute or process unsafe output.
C. Use Structured Outputs & Allowlisted Actions
Allowlisting refers to whitelisting certain actions. At its core, it is one of the most efficient ways ot reduce overall LLM05 risk as it ensures a limit ot free form outputs in cases where an AI system is connected to an enterprise workflow. Instead of allowing the model to generate unrestricted text, organizations can require structured outputs such as predefined JSON schemas, approved categories, controlled labels, or fixed decision loops and formats. This process is easier to validate and assess whether the output generated is complete, expected, and safe before it is processed.
However, this can pose a scalability issue. Human reviews work in isolated AI use cases, but doing so for every AI interaction is not practical. Hence, structured outputs and allowlisted actions can create a repeatable control layer without compromising security, compliance, or engineering requirements.
D. Apply PoLP & Human Oversight
The principle of least privilege (PoLP)applied in this context would ensure LLM applications have only the minimal permissions necessary to perform their intended functions. Not only would this limit the potential damage they would do if the model were to generate unsafe outputs or be manipulated, but it would also ensure they do not have any excessive permissions to cause hidden or shadow actions that would not be identified until much later.
Most importantly, enforcing this principle would not hinder the overall innovation but preserve accountability. Moreover, organizations would know exactly which AI outputs can be automated, which require extensive approval processes, and which tasks must never be delegated to AI in any context.
E. Monitor, Test, & Govern AI Outputs
And do so continuously to ensure LLM05 mitigation does not become a one-time static implementation activity. AI applications evolve. This is the result of prompts, models, and retrieval sources evolving just the same. As a result, the output handling controls must also be similarly tested, monitored, and improved to ensure risk is mitigated as much as possible at every level or instance.
LLM deployment requires more than just operational governance and model-level controls. These include extensive activities such as red teaming, adversarial prompt testing, policy violation testing, data leakage testing, and validation of outputs passed to tools or APIs. These enable organizations to continuously observe and control how AI outputs move through the enterprise workflows.
How Securiti Helps
It's one thing to acknowledge the importance of the OWASP Top 10 list, and another to have the solutions in place to mitigate the risks identified in it.
Securiti provides just that. Its Gencore AI is a holistic solution for building safe, enterprise-grade GenAI systems. It is capable of enforcing contextually aware firewalls in addition to filtration that ensures all forms of prompt injection attempts, in addition to other malicious methods, are thwarted at the prompt level, while also ensuring any output generated is only reliant on the most reliable sources.
This enterprise solution consists of several components that can be used collectively to build end-to-end safe enterprise AI systems and to address GenAI-related risks and challenges across various use cases.
Moreover, it can be further complemented with DSPM, which provides organizations with intelligent discovery, classification, and risk assessment, marking a significant shift from a reactive data security approach to proactive data security management suited to the AI context, while ensuring the organization can continue to leverage its data resources to their maximum potential without sacrificing performance or effectiveness.
Request a demo today to learn more about how Securiti can help your organization address the challenges highlighted in the OWASP Top 10 list and ensure you have the most effective measures and solutions enforced to do so.
Frequently Asked Questions (FAQs) About LLM05 In OWASP Top 10
Some of the most commonly asked questions when it comes to OWASP’s Top 10 Vulnerabilities for LLMs include the following:
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