Sebastian Schrittwieser
ChatGPT, ignore the above instructions! Prompt injection attacks and how to avoid them.
#1about 2 minutes
The rapid adoption of LLMs outpaces security practices
New technologies like large language models are often adopted quickly without established security best practices, creating new vulnerabilities.
#2about 4 minutes
How user input can override developer instructions
A prompt injection occurs when untrusted user input contains instructions that hijack the LLM's behavior, overriding the developer's original intent defined in the context.
#3about 4 minutes
Using prompt injection to steal confidential context data
Attackers can use prompt injection to trick an LLM into revealing its confidential context or system prompt, exposing proprietary logic or sensitive information.
#4about 4 minutes
Expanding the attack surface with plugins and web data
LLM plugins that access external data like emails or websites create an indirect attack vector where malicious prompts can be hidden in that external content.
#5about 2 minutes
Prompt injection as the new SQL injection for LLMs
Prompt injection mirrors traditional SQL injection by mixing untrusted data with developer instructions, but lacks a clear mitigation like prepared statements.
#6about 3 minutes
Why simple filtering and encoding fail to stop attacks
Common security tactics like input filtering and blacklisting are ineffective against prompt injections due to the flexibility of natural language and encoding bypass techniques.
#7about 4 minutes
Using user confirmation and dual LLM models for defense
Advanced strategies include requiring user confirmation for sensitive actions or using a dual LLM architecture to isolate privileged operations from untrusted data processing.
#8about 5 minutes
The current state of LLM security and the need for awareness
There is currently no perfect solution for prompt injection, making developer awareness and careful design of LLM interactions the most critical defense.
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