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Manipulating LLMs via crafted inputs can lead to unauthorized access, data breaches, and compromised decision-making.
Neglecting to validate LLM outputs may lead to downstream security exploits, including code execution that compromises systems and exposes data.
Tampered training data can impair LLM models leading to responses that may compromise security, accuracy, or ethical behavior.
Overloading LLMs with resource-heavy operations can cause service disruptions and increased costs.
Depending upon compromised components, services or datasets undermine system integrity, causing data breaches and system failures.
Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage.
LLM plugins processing untrusted inputs and having insufficient access control risk severe exploits like remote code execution.
Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust.
Failing to critically assess LLM outputs can lead to compromised decision making, security vulnerabilities, and legal liabilities.
Unauthorized access to proprietary large language models risks theft, competitive advantage, and dissemination of sensitive information.