LLM Output Handling and Privacy Risks Tryhackme Walkthrough

In traditional web security, we often think about inputs as the main attack surface, such as SQL injection, XSS, command injection, and other similar attacks. But with LLMs, outputs are just as important.

An LLM might generate a response that is later processed by another system, displayed to a user, or used to trigger an automated action. If that output isn't validated or sanitised, it can lead to serious issues such as:

• Injection attacks downstream - for example, an LLM accidentally generating HTML or JavaScript that gets rendered directly in a web application.

• Prompt-based escalation - where model output includes hidden instructions or data that manipulate downstream systems.

• Data leakage - if the LLM outputs sensitive tokens, API keys, or internal knowledge that should never leave the model.

LLMs often have access to far more data than a single user might expect. They may be trained on sensitive content, have access to internal knowledge bases, or interact with backend services. If their output isn't carefully controlled, they might reveal information unintentionally, such as:

• Internal URLs, API endpoints, or infrastructure details.

• User data is stored in past conversations or logs.

• Hidden system prompts or configuration secrets that are used to guide the model's behaviour.

Attackers can exploit this by crafting queries designed to trick the model into leaking data, sometimes without the system owners even realising it.

In traditional application security, developers are taught to never trust user input; it should always be validated, sanitised, and handled carefully before being processed. When it comes to LLM-powered applications, the same principle applies, but there's a twist: instead of user input, it's often the model's output that becomes the new untrusted data source.

Improper output handling refers to situations where a system blindly trusts whatever the LLM generates and uses it without verification, filtering, or sanitisation. While this might sound harmless, it becomes a problem when the generated content is:

• Directly rendered in a browser, for example, by injecting raw text into a web page without escaping.

• Embedded in templates or scripts, where the model output is used to dynamically generate server-side pages or messages.

• Passed to automated processes, such as a CI/CD pipeline, API client, or database query builder that executes whatever the model produces.

Because LLMs can output arbitrary text, including code, scripts, and commands, treating those outputs as “safe” can easily lead to security vulnerabilities.

Common Places Where This Happens

Improper output handling can creep into an LLM-integrated system in several ways. Here are the most common:

Frontend Rendering

A chatbot's response is inserted directly into a page with innerHTML, allowing an attacker to inject malicious HTML or JavaScript if the model ever returns something unsafe.

Server-Side Templates

Some applications use model output to populate templates or build views. If that output contains template syntax (like Jinja2 or Twig expressions), it might trigger server-side template injection (SSTI).

Automated Pipelines

In more advanced use cases, LLMs might generate SQL queries, shell commands, or code snippets that are executed automatically by backend systems. Without validation, this can result in command injection, SQL injection, or execution of unintended logic.

Real-World Consequences

Improperly handled LLM output isn't just a theoretical risk; it can have serious consequences:

DOM-Based XSS

If a chatbot suggests a piece of HTML and it's rendered without escaping, an attacker might craft a prompt that causes the model to generate a <script> tag, leading to cross-site scripting.

Template Injection

If model output is embedded into a server-side template without sanitisation, it could lead to remote code execution on the server.

Accidental Command Execution

In developer tools or internal automation pipelines, generated commands might be run directly in a shell. A carefully crafted prompt could cause the LLM to output a destructive command (such as rm -rf /) that executes automatically.

Answer the questions below :

What vulnerability refers to situations where a system blindly trusts whatever the LLM generates and uses it without verification, filtering, or sanitisation?

Improper Output Handling

What is the content of flag.txt?

THM{LLM_c0mmand_3xecution_1s_r34l}

Conclusion

In this room, we've looked at two of the most overlooked but impactful risks when working with LLMs: Improper Output Handling (LLM05) and Sensitive Information Disclosure (LLM02). While much of the focus in LLM security is often on inputs and prompt manipulation, outputs can be just as dangerous and sometimes even easier for attackers to exploit.

Recap of What We Covered

Improper Output Handling (LLM05):

We explored how trusting raw model output, whether HTML, template code, or system commands, can lead to downstream attacks like DOM XSS, template injection, or arbitrary command execution. The key lesson: model output should always be treated as untrusted input.

Sensitive Information Disclosure (LLM02):

We saw how LLMs can unintentionally leak sensitive data from their training sets, runtime context, previous conversations, or even their own system prompts. These disclosures often don't require exploitation of a bug, just clever manipulation of the model's behaviour.

Real Attack Scenarios:
Through practical examples, we demonstrated how attackers can weaponise LLM outputs to gain access, escalate privileges, or exfiltrate data.

By now, you should have a solid understanding of how LLM outputs can become an attack surface and how to defend against them. Whether you're building LLM-powered applications or testing them as part of a security assessment, always remember: outputs deserve the same scrutiny as inputs.