OWASP Top 10 for Large Language Model Applications | OWASP Foundation
OWASP Top 10 for Large Language Model Applications | OWASP Foundation
Aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing Large Language Models (LLMs)
Below detail at this link: https://owasp.org/www-project-top-10-for-large-language-model-applications/descriptions/
This is a draft list of important vulnerability types for Artificial Intelligence (AI) applications built on Large Language Models (LLMs) LLM01:2023 - Prompt Injections
Description: Bypassing filters or manipulating the LLM using carefully crafted prompts that make the model ignore previous instructions or perform unintended actions. LLM02:2023 - Data Leakage
Description: Accidentally revealing sensitive information, proprietary algorithms, or other confidential details through the LLM’s responses. LLM03:2023 - Inadequate Sandboxing
Description: Failing to properly isolate LLMs when they have access to external resources or sensitive systems, allowing for potential exploitation and unauthorized access. LLM04:2023 - Unauthorized Code Execution
Description: Exploiting LLMs to execute malicious code, commands, or actions on the underlying system through natural language prompts. LLM05:2023 - SSRF Vulnerabilities
Description: Exploiting LLMs to perform unintended requests or access restricted resources, such as internal services, APIs, or data stores. LLM06:2023 - Overreliance on LLM-generated Content
Description: Excessive dependence on LLM-generated content without human oversight can result in harmful consequences. LLM07:2023 - Inadequate AI Alignment
Description: Failing to ensure that the LLM’s objectives and behavior align with the intended use case, leading to undesired consequences or vulnerabilities. LLM08:2023 - Insufficient Access Controls
Description: Not properly implementing access controls or authentication, allowing unauthorized users to interact with the LLM and potentially exploit vulnerabilities. LLM09:2023 - Improper Error Handling
Description: Exposing error messages or debugging information that could reveal sensitive information, system details, or potential attack vectors. LLM10:2023 - Training Data Poisoning
Description: Maliciously manipulating training data or fine-tuning procedures to introduce vulnerabilities or backdoors into the LLM.
