Findings
Prone to harmful content
Updated: June 19, 2025
Description
The AI model can be manipulated into generating content that is harmful, offensive, or inappropriate, violating ethical standards and safety policies.
This vulnerability arises when the model is prompted with malicious, manipulative, or harmful input, leading it to produce outputs that could be damaging to individuals, groups, or organizations.
Example Attack
This vulnerability could lead to the generation of toxic content, including hate speech, discriminatory language, or harmful instructions. This could result in reputational damage, violation of community standards, or legal consequences. Additionally, harmful content may encourage harmful behavior or create a negative user experience.
Remediation
To mitigate this risk, investigate and enhance the effectiveness of guardrails and output security mechanisms to prevent the generation of harmful, offensive, or inappropriate content. Strengthen content filtering systems, refine model training, and employ better context understanding to ensure that harmful prompts do not trigger dangerous outputs. Ongoing monitoring and regular updates to the safety protocols should be conducted to prevent exploitation
Security Frameworks
Improper Output Handling refers specifically to insufficient validation, sanitization, and handling of the outputs generated by large language models before they are passed downstream to other components and systems. Since LLM-generated content can be controlled by prompt input, this behavior is similar to providing users indirect access to additional functionality.
Adversaries may abuse their access to a victim system and use its resources or capabilities to further their goals by causing harms external to that system. These harms could affect the organization (e.g. Financial Harm, Reputational Harm), its users (e.g. User Harm), or the general public (e.g. Societal Harm).
Reputational harm involves a degradation of public perception and trust in organizations. Examples of reputation-harming incidents include scandals or false impersonations.
Societal harms might generate harmful outcomes that reach either the general public or specific vulnerable groups such as the exposure of children to vulgar content.
User harms may encompass a variety of harm types including financial and reputational that are directed at or felt by individual victims of the attack rather than at the organization level.
An adversary may use a carefully crafted LLM Prompt Injection designed to place LLM in a state in which it will freely respond to any user input, bypassing any controls, restrictions, or guardrails placed on the LLM. Once successfully jailbroken, the LLM can be used in unintended ways by the adversary.