  LLM app stores have seen rapid growth, leading to the proliferation of
numerous custom LLM apps. However, this expansion raises security concerns. In
this study, we propose a three-layer concern framework to identify the
potential security risks of LLM apps, i.e., LLM apps with abusive potential,
LLM apps with malicious intent, and LLM apps with exploitable vulnerabilities.
Over five months, we collected 786,036 LLM apps from six major app stores: GPT
Store, FlowGPT, Poe, Coze, Cici, and Character.AI. Our research integrates
static and dynamic analysis, the development of a large-scale toxic word
dictionary (i.e., ToxicDict) comprising over 31,783 entries, and automated
monitoring tools to identify and mitigate threats. We uncovered that 15,146
apps had misleading descriptions, 1,366 collected sensitive personal
information against their privacy policies, and 15,996 generated harmful
content such as hate speech, self-harm, extremism, etc. Additionally, we
evaluated the potential for LLM apps to facilitate malicious activities,
finding that 616 apps could be used for malware generation, phishing, etc. Our
findings highlight the urgent need for robust regulatory frameworks and
enhanced enforcement mechanisms.
