#llm-security

#llm-security

LLM systems face threats from prompt injection and stealing, necessitating robust security measures.

System prompts are public and should be treated as such to mitigate risks.

Security strategies include embedding instructions, adversarial detectors, and fine-tuning models.

Fine-tuning, quantization, and guardrails play crucial roles in mitigating vulnerabilities of LLMs against jailbreaking attacks.

LLM security is vital for data scientists to ensure trust and prevent data breaches.

LLM security is vital for maintaining user trust and preventing data breaches due to hallucinations.