LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins

Third-party apps in LLM platforms pose security risks: The paper identifies that ChatGPT plugins and similar third-party integrations cannot be implicitly trusted, creating a new attack surface where arbitrary developers can potentially compromise user security and privacy through these extensions.

Natural language creates ambiguity vulnerabilities: Because plugins communicate with LLM platforms and users through natural language, which can be interpreted in multiple ways, this imprecision introduces security gaps that traditional software interfaces don't face—attackers can exploit these misunderstandings to manipulate system behavior.

Multiple stakeholders can become attack vectors: The research reveals that different parties in the LLM ecosystem (platform designers, plugin developers, and users) each have capabilities that could be leveraged to attack others, meaning security threats come from multiple directions rather than just external hackers.

Concrete vulnerabilities were discovered in existing plugins: The authors applied their framework to OpenAI's actual plugin ecosystem and found real examples of plugins demonstrating the security issues outlined in their taxonomy, indicating these aren't theoretical concerns but present-day risks.

Systematic evaluation framework is needed: The paper proposes that LLM platform designers need formal security evaluation methods to assess and improve the safety, privacy, and security of plugin ecosystems before widespread adoption, rather than addressing problems reactively.