Reverse Shell - AI Security Technique

Researchers identified 20 remote code execution (RCE) vulnerabilities across 11 different LLM frameworks. They discovered applications deployed on the public internet built using these LLM frameworks and demonstrated the RCE vulnerabilities could be exploited using prompt injection.

The 11 LLM frameworks the researchers evaluated were: LangChain, LlamaIndex, Pandas-ai, Langflow, Pandas-llm, Auto-GPT, Griptape, Lagent, MetaGPT, vanna, and langroid.

Researchers at ReversingLabs have identified malicious models containing embedded malware hosted on the Hugging Face model repository. The models were found to execute reverse shells when loaded, which grants the threat actor command and control capabilities on the victim's system. Hugging Face uses Picklescan to scan models for malicious code, however these models were not flagged as malicious. The researchers discovered that the model files were seemingly purposefully corrupted in a way that the malicious payload is executed before the model ultimately fails to de-serialize fully. Picklescan relied on being able to fully de-serialize the model.

Since becoming aware of this issue, Hugging Face has removed the models and has made changes to Picklescan to catch this particular attack. However, pickle files are fundamentally unsafe as they allow for arbitrary code execution, and there may be other types of malicious pickles that Picklescan cannot detect.

threlfall_hax, a security researcher, created organization accounts on Hugging Face, a public model repository, that impersonated real organizations. These false Hugging Face organization accounts looked legitimate so individuals from the impersonated organizations requested to join, believing the accounts to be an official site for employees to share models. This gave the researcher full access to any AI models uploaded by the employees, including the ability to replace models with malicious versions. The researcher demonstrated that they could embed malware into an AI model that provided them access to the victim organization's environment. From there, threat actors could execute a range of damaging attacks such as intellectual property theft or poisoning other AI models within the victim's environment.