Keno Dreßel

Prompt Injection, Poisoning & More: The Dark Side of LLMs

How can a simple chatbot be turned into a hacker? Explore the critical security risks of LLMs, from prompt injection to data poisoning.

Prompt Injection, Poisoning & More: The Dark Side of LLMs
#1about 5 minutes

Understanding and mitigating prompt injection attacks

Prompt injection manipulates LLM outputs through direct or indirect methods, requiring mitigations like restricting model capabilities and applying guardrails.

#2about 6 minutes

Protecting against data and model poisoning risks

Malicious or biased training data can poison a model's worldview, necessitating careful data screening and keeping models up-to-date.

#3about 6 minutes

Securing downstream systems from insecure model outputs

LLM outputs can exploit downstream systems like databases or frontends, so they must be treated as untrusted user input and sanitized accordingly.

#4about 4 minutes

Preventing sensitive information disclosure via LLMs

Sensitive data used for training can be extracted from models, highlighting the need to redact or anonymize information before it reaches the LLM.

#5about 1 minute

Why comprehensive security is non-negotiable for LLMs

Just like in traditional application security, achieving 99% security is still a failing grade because attackers will find and exploit any existing vulnerability.

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