GHSA-9w88-8rmg-7g2p: picklescan: scan bypass allows silent RCE via ML models

### Summary Using cProfile.runctx function, which is a built-in python library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to cProfile.runctx function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import cProfile class EvilCProfileRunctx: def __reduce__(self): # cProfile.runctx(cmd, globals, locals) -> exec(cmd, ...) return cProfile.runctx, ("__import__('os').system('whoami')", None, None) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects. ### Corresponding https://github.com/FredericDT https://github.com/Qhaoduoyu

An attacker publishes a seemingly legitimate PyTorch sentiment analysis model on HuggingFace. The model file contains an EvilCProfileRunctx payload embedded in the pickle using cProfile.runctx to execute a reverse shell. A security-conscious ML team runs picklescan before loading any external models — the scan returns clean. They load the model in their GPU training server, triggering RCE. The attacker now has a shell in the ML infrastructure with access to training data, model weights, API keys stored in environment variables, and potentially cloud credentials. The entire compromise is silent — no scanner alert, no anomalous file detected.