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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