OWASP Top 10 for LLM Applications

OWASP Top 10 for Large Language Model Applications version 1.1

LLM01: Prompt Injection

Manipulating LLMs via crafted inputs can lead to unauthorized access, data breaches, and compromised decision-making.

LLM02: Insecure Output Handling

Neglecting to validate LLM outputs may lead to downstream security exploits, including code execution that compromises systems and exposes data.

LLM03: Training Data Poisoning

Tampered training data can impair LLM models leading to responses that may compromise security, accuracy, or ethical behavior.

LLM04: Model Denial of Service

Overloading LLMs with resource-heavy operations can cause service disruptions and increased costs.

LLM05: Supply Chain Vulnerabilities

Depending upon compromised components, services or datasets undermine system integrity, causing data breaches and system failures.

LLM06: Sensitive Information Disclosure

Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage.

LLM07: Insecure Plugin Design

LLM plugins processing untrusted inputs and having insufficient access control risk severe exploits like remote code execution.

LLM08: Excessive Agency

Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust.

LLM09: Overreliance

Failing to critically assess LLM outputs can lead to compromised decision making, security vulnerabilities, and legal liabilities.

LLM10: Model Theft

Unauthorized access to proprietary large language models risks theft, competitive advantage, and dissemination of sensitive information.

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REFERENCES

• https://owasp.org/www-project-top-10-for-large-language-model-applications/