CVE-2025-1944 in picklescan
Summary
by MITRE • 03/10/2025
picklescan before 0.0.23 is vulnerable to a ZIP archive manipulation attack that causes it to crash when attempting to extract and scan PyTorch model archives. By modifying the filename in the ZIP header while keeping the original filename in the directory listing, an attacker can make PickleScan raise a BadZipFile error. However, PyTorch's more forgiving ZIP implementation still allows the model to be loaded, enabling malicious payloads to bypass detection.
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Analysis
by VulDB Data Team • 12/29/2025
CVE-2025-1944 represents a critical vulnerability in picklescan version 0.0.22 and earlier, where the tool fails to properly validate ZIP archive structures during PyTorch model scanning operations. This vulnerability stems from an insufficient validation mechanism that does not adequately verify the consistency between filename entries in the ZIP central directory and the actual file headers. The flaw specifically manifests when an attacker manipulates the filename within the ZIP header while maintaining the original filename in the directory listing, creating a mismatch that triggers a BadZipFile exception. This behavior directly relates to CWE-161, which addresses insufficient validation of filename consistency in archive files, and aligns with ATT&CK technique T1059.007 for execution through scripting languages where the vulnerability enables bypass of security controls. The technical implementation of this attack exploits the difference in ZIP parsing behavior between picklescan and PyTorch's native implementation, where PyTorch's more permissive approach allows the malicious archive to load successfully despite picklescan's failure to process it. This discrepancy creates a false sense of security as the vulnerability allows malicious payloads to evade detection mechanisms that rely on picklescan for scanning PyTorch model files. The operational impact of this vulnerability extends beyond simple denial of service, as it enables attackers to craft malicious PyTorch models that appear benign to security scanners but contain harmful payloads that execute when the model is loaded. The vulnerability particularly affects environments that rely on automated scanning of machine learning model files, where the inconsistent behavior between different ZIP parsing implementations creates a security gap that attackers can exploit. Organizations using picklescan for security validation of PyTorch models face significant risk as this vulnerability allows bypass of security controls without detection, potentially enabling code execution or data exfiltration through malicious model files. The attack vector specifically targets the validation phase of model scanning, where the security tool's strict validation fails while the target system's more lenient parsing allows execution, creating a window for malicious activity. This vulnerability demonstrates the importance of consistent validation approaches across different components in security toolchains and highlights the risks associated with relying on multiple tools with varying levels of strictness in file validation. The recommended mitigation involves upgrading to picklescan version 0.0.23 or later, which includes improved ZIP header validation and consistent filename verification that prevents the manipulation attack. Additionally, organizations should implement layered security approaches that validate file integrity through multiple independent tools and consider implementing additional checks beyond simple file extension validation. Security teams should also monitor for similar vulnerabilities in other tools that handle ZIP archives and ensure that validation mechanisms are robust enough to prevent similar inconsistencies between different parsing implementations.