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Large Language Models Penetration Testing

LLM01:2023 – Prompt Injections
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
Accidentally revealing sensitive information, proprietary algorithms, or other confidential details through the LLM’s responses.

LLM03:2023 – Inadequate Sandboxing
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
Exploiting LLMs to execute malicious code, commands, or actions on the underlying system through natural language prompts.
LLM05:2023 – SSRF Vulnerabilities
Exploiting LLMs to perform unintended requests or access restricted resources, such as internal services, APIs, or data stores.
LLM06:2023 – Over reliance on LLM-generated Content
Excessive dependence on LLM-generated content without human oversight can result in harmful consequences.
LLM07:2023 – Inadequate AI Alignment
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
Not properly implementing access controls or authentication, allowing unauthorized users to interact with the LLM and potentially exploit vulnerabilities.

LLM09:2023 – Improper Error Handling
Exposing error messages or debugging information that could reveal sensitive information, system details, or potential attack vectors.

LLM10:2023 – Training Data Poisoning
Maliciously manipulating training data or fine-tuning procedures to introduce vulnerabilities or backdoors into the LLM.


OWASP lists the ten most critical large language model vulnerabilities

  • The list highlights the business impact and plurality of the ten most critical vulnerabilities found in artificial intelligence applications based on LLMs such as ChatGPT, Google’s BARD, and Microsoft’s CoPilot.

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