When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
Xinyue Shen, Yun Shen, Michael Backes, Yang Zhang

TL;DR
This paper conducts a comprehensive risk assessment of knowledge file leakage in GPTs, identifying five leakage vectors and demonstrating a high success rate of privilege escalation via the Code Interpreter tool, with significant implications for data security.
Contribution
It introduces a novel workflow inspired by DSPM for assessing GPT knowledge file leakage and uncovers multiple leakage vectors and a privilege escalation vulnerability.
Findings
Identified five key leakage vectors in GPTs.
Demonstrated a 95.95% success rate in privilege escalation via Code Interpreter.
Found that 28.80% of leaked files are copyrighted or sensitive.
Abstract
Knowledge files have been widely used in large language model (LLM) agents, such as GPTs, to improve response quality. However, concerns about the potential leakage of knowledge files have grown significantly. Existing studies demonstrate that adversarial prompts can induce GPTs to leak knowledge file content. Yet, it remains uncertain whether additional leakage vectors exist, particularly given the complex data flows across clients, servers, and databases in GPTs. In this paper, we present a comprehensive risk assessment of knowledge file leakage, leveraging a novel workflow inspired by Data Security Posture Management (DSPM). Through the analysis of 651,022 GPT metadata, 11,820 flows, and 1,466 responses, we identify five leakage vectors: metadata, GPT initialization, retrieval, sandboxed execution environments, and prompts. These vectors enable adversaries to extract sensitive…
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Taxonomy
TopicsCloud Data Security Solutions
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Cosine Annealing · Linear Layer · Layer Normalization · Adam · Dense Connections · Linear Warmup With Cosine Annealing · Attention Dropout · Softmax · Weight Decay
