What is LLM Security?
LLM Security lLM security focuses on protecting large language models and systems built on them from attacks, misuse, and unintended behaviors. It addresses vulnerabilities unique to language models like prompt injection, training data attacks, and model extraction.
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What is LLM Security?
LLM (Large Language Model) security is the discipline of securing AI systems based on language models like GPT-4, Claude, or Llama. It addresses threats specific to how these models work: prompt injection attacks that manipulate model behavior, training data poisoning that corrupts model knowledge, model extraction attacks that steal proprietary models, and jailbreaking that bypasses safety measures. LLM security also covers the systems built around models—the applications, agents, and integrations that use LLMs as components.
How LLM Security Works
LLM security combines multiple approaches: input validation screens prompts for attacks; output monitoring detects problematic responses; system prompt protection prevents leakage of confidential instructions; rate limiting prevents abuse; access controls restrict model capabilities; and continuous monitoring detects attacks in progress. Security also extends to the model development lifecycle: securing training data, protecting model weights, and ensuring deployment infrastructure is hardened. Red teaming and security testing probe for vulnerabilities before attackers find them.
Why LLM Security Matters
LLMs are increasingly central to AI applications, and their unique architecture creates unique vulnerabilities. Traditional security controls weren't designed for models that interpret natural language instructions—they can't distinguish between legitimate requests and cleverly crafted attacks. As LLMs gain more capabilities and access to more systems, the stakes of LLM security grow. A compromised LLM can lead to data breaches, unauthorized actions, reputation damage, and safety incidents. Securing LLMs is essential for realizing their benefits while managing their risks.
Examples of LLM Security
Security testing reveals that a specific prompt format bypasses an LLM's refusal to generate harmful content—the vulnerability is patched in the system prompt. Monitoring detects that an attacker is systematically probing the LLM to extract its training data, and access is revoked. A corporate LLM deployment includes input filtering, output monitoring, and rate limiting as defense in depth. Red team exercises find that the LLM will reveal its system prompt under certain conditions, prompting security improvements.
Key Takeaways
- 1LLM Security is a critical concept in AI agent security and observability.
- 2Understanding llm security is essential for developers building and deploying autonomous AI agents.
- 3Moltwire provides tools for monitoring and protecting against threats related to llm security.
Written by the Moltwire Team
Part of the AI Security Glossary · 25 terms
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