Securing LLM Outputs: Preventing Insecure Output Handling and Injection Attacks

Background: The threat landscape in the LLM era has become hybrid compared with the classic OWASP Top Ten. Interconnected LLM services have brought new types of attacks, and the boundaries between them have somewhat disappeared. We cannot always trust LLM output, and sometimes LLMs can generate harmful content.

Insecure Output Handling Scenario: Let imagine we have model which is service our customer over chat component . Some days later, an attacker was somehow able to change the structure of our frontend or backend, whose purpose is to sanitize prompts, and added special logic or text to the input text. After customers interacted with our chat, and because the attacker added a new default prompt for each input, the LLM would return an XSS payload to the customer.

Insecure Output Handling Prevention:

• Always follow to owasp asvs Sanitization
• Apply guardrails for LLM

Conclusion: As the attack surface now spans both application logic and model behavior, organizations must treat LLM output as untrusted data. Strong input/output sanitization, strict content validation, and layered guardrails are essential to reduce risk and protect users from downstream exploitation.