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The comparison for picklescan 0.0.32 is incomplete. Review the confirmed matches below.
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Vulnerabilities: 20
picklescan before 0.0.34 fails to detect the _operator.methodcaller built-in function when scanning pickle files for malicious code. Attackers can craft malicious pickle payloads using _operator.methodcaller that evade detection and execute arbitrary code when loaded by pickle.load().
picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.
Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran.getlincoef gadget in pickle __reduce__ methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded, bypassing Picklescan's safety checks and enabling supply-chain poisoning of shared model files.
picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file.
picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.
picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data.
picklescan before 1.0.4 fails to block at least seven Python standard library modules (including uuid, _osx_support, _aix_support, _pyrepl.pager, and imaplib) exposing eight functions that provide direct arbitrary command execution. Attackers can craft malicious pickle files importing these unblocked modules to achieve remote code execution while bypassing picklescan's safety validation entirely.
picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded.
Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran._eval_length gadget in pickle __reduce__ methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded by victims who trust Picklescan's safety validation.
picklescan before 1.0.1 contains an unsafe pickle deserialization vulnerability allowing unauthenticated attackers to create arbitrary zero-byte files via logging.FileHandler class instantiation. Attackers can exploit this by crafting malicious pickle payloads to bypass RCE blocklists and create lock files or other filesystem artifacts, potentially causing denial of service or application disruption.
picklescan before 1.0.3 contains a scanning bypass vulnerability in the scan_pytorch function that allows attackers to embed malicious magic numbers via dynamic eval using the __reduce__ trick. Attackers can craft malicious PyTorch payloads that evade picklescan detection while remaining executable, enabling arbitrary code execution when loaded with torch.load().
picklescan before 1.0.1 contains an unsafe deserialization vulnerability allowing unauthenticated users to execute arbitrary code by hiding eval calls nested under callable objects via getattr. Attackers can embed malicious code in pickle files that evades detection but executes when the pickle is loaded from untrusted sources.
picklescan before 1.0.4 contains an incomplete blocklist for the profile module that fails to block the module-level profile.run() function, allowing attackers to achieve arbitrary code execution via exec(). Attackers can craft malicious pickle files calling profile.run(statement) to execute arbitrary Python code while picklescan reports zero security issues.
picklescan before 0.0.35 contains an unsafe pickle deserialization vulnerability allowing unauthenticated attackers to read arbitrary server files by chaining io.FileIO and urllib.request.urlopen. Attackers can bypass RCE-focused blocklists to exfiltrate sensitive data like /etc/passwd to external servers.
picklescan before 1.0.4 fails to block pkgutil.resolve_name, allowing attackers to bypass the entire blocklist by resolving any dangerous function through indirect REDUCE calls. Remote attackers can invoke any blocked function such as os.system, builtins.exec, or subprocess.call to achieve remote code execution.
picklescan before 0.0.33 fails to block the ctypes module, allowing attackers to achieve remote code execution by invoking direct syscalls and accessing raw memory. Attackers can craft malicious pickle files using ctypes.WinDLL to load kernel32.dll and execute arbitrary commands, bypassing sandbox protections and gadget chain detection.
PickleScan before 0.0.33 fails to include the pty.spawn function in its unsafe globals list, allowing attackers to bypass security checks. Malicious actors can craft pickle payloads using pty.spawn to achieve arbitrary code execution when files are processed by PickleScan.
picklescan before 0.0.33 contains an arbitrary file writing vulnerability that allows attackers to bypass the dangerous blocklist by using distutils.file_util.write_file. Attackers can construct malicious pickle objects to overwrite critical system files and achieve denial of service or remote code execution.
picklescan before 0.0.33 contains an incomplete deny-list that fails to block pydoc.locate and operator.methodcaller functions, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using these unblocked functions to achieve arbitrary code execution when the pickle is deserialized.
### Summary Using pty.spawn, which is a built-in python library function to execute arbitrary commands on the host system. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to `pty.spawn` function in the `__reduce__` method. Then the victim attempts to use picklescan to scan the pickle file for issues and sees this - ``` ----------- SCAN SUMMARY ----------- Scanned files: 1 Infected files: 0 Dangerous globals: 0 ``` The victim proceeds to load the pickle file and execute attacker-injected arbitrary code. ### PoC ``` class PtyExploit: def __reduce__(self): return (pty.spawn
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