Attack Chain

The provided source describes a vulnerability in picklescan's detection capabilities rather than a multi-stage attack chain in the wild. The exploitation scenario primarily involves the delivery and deserialization of a specially crafted malicious file. Therefore, a multi-step attack chain as observed in active campaigns cannot be accurately constructed from this information.

However, the core exploitation flow is as follows:

1. Initial Access: An attacker delivers a malicious pickle file to a victim system (e.g., via email, download from an untrusted source, or compromised data pipeline).
2. Defense Evasion (Bypass picklescan): The malicious pickle file is crafted to invoke the torch.utils.bottleneck.__main__.run_autograd_prof function, which picklescan versions < 0.0.30 fail to identify as malicious.
3. Deserialization: The victim application or system attempts to load and deserialize the seemingly benign pickle file, potentially after a failed picklescan check.
4. Arbitrary Code Execution: During the deserialization process, the embedded malicious code within the pickle file is executed in the context of the application.
5. Impact: The executed code performs actions determined by the attacker, such as system compromise, data exfiltration, or further malware deployment.

Impact

The successful exploitation of CVE-2025-71345 results in unauthenticated remote code execution on systems that process untrusted pickle files with vulnerable versions of picklescan. This allows attackers to bypass security measures, gain full control over the affected system, steal sensitive data, deploy ransomware, or establish persistence within the network. The vulnerability has a CVSS v3.1 base score of 8.1 (High), underscoring the severe consequences of exploitation, particularly in environments handling machine learning models or other serialized Python objects where picklescan is deployed for security scanning.

Recommendation

• Patch CVE-2025-71345 immediately: Update picklescan to version 0.0.30 or newer to mitigate the vulnerability and ensure proper detection of malicious pickle files.
• Implement Secure Deserialization Practices: Restrict the deserialization of pickle files from untrusted or unverified sources, as described by CWE-502.
• Educate Users: Train users and developers about the risks associated with handling untrusted serialized data, including pickle files.

Attack Chain

1. Attacker crafts a malicious Python pickle file containing arbitrary code, specifically exploiting the lib2to3.pgen2.pgen.ParserGenerator.make_label function's reduce method to bypass picklescan's detection.
2. The malicious pickle file is delivered to a target system, potentially through methods such as email attachments, malicious web downloads, or integration into a compromised software supply chain.
3. An application or user on the target system processes or scans the received pickle file using picklescan version prior to 0.0.30.
4. Due to CVE-2025-71343, picklescan fails to identify the embedded malicious payload, incorrectly marking the file as benign.
5. A Python application or script on the target system subsequently loads the "undetected" malicious pickle file using pickle.load().
6. During the deserialization process, the embedded arbitrary code within the pickle file is executed in the context of the Python application.
7. The attacker achieves arbitrary command execution on the compromised system, potentially leading to full system compromise, data exfiltration, or further lateral movement.

Impact

The successful exploitation of CVE-2025-71343 allows an attacker to achieve arbitrary code execution on systems that process untrusted pickle files using vulnerable versions of picklescan. This can lead to complete system compromise, unauthorized access to sensitive data, installation of malware, or disruption of services. While no specific victim counts are provided, any organization or individual processing Python pickle files in environments where picklescan is used for security vetting (especially in data science, machine learning, or software development contexts) is at risk. The undetected nature of the attack makes it particularly dangerous, as security tools designed to prevent such threats are bypassed.

Recommendation

• Update picklescan to version 0.0.30 or later immediately to patch CVE-2025-71343 and address the detection bypass vulnerability.
• Implement strict controls on the ingestion and processing of untrusted pickle files, regardless of picklescan's output, especially from external or unverified sources.
• Educate users and developers about the risks associated with deserializing untrusted data, specifically in the context of Python pickle files, to prevent arbitrary code execution (CWE-502).

Attack Chain

1. An attacker crafts a specially designed Python pickle file containing arbitrary code.
2. The malicious code is embedded within the pickle file's reduce methods, specifically leveraging idlelib.run.Executive.runcode to obscure its true intent.
3. The attacker then distributes this malicious pickle file, potentially by injecting it into a software supply chain (e.g., a shared machine learning model repository).
4. A victim system or application, such as a PyTorch model loader, downloads or receives the compromised pickle file.
5. The application attempts to deserialize the pickle file using Python's pickle.load() function.
6. During the deserialization process, picklescan (if present and vulnerable, i.e., version < 0.0.30) fails to identify the idlelib.run.Executive.runcode as a malicious primitive.
7. The embedded arbitrary code within the pickle file's reduce methods is consequently executed by the Python interpreter on the victim's system.
8. This results in remote code execution (RCE), allowing the attacker to compromise the host system and potentially exfiltrate data or establish persistence.

Impact

The successful exploitation of CVE-2025-71342 carries severe consequences, primarily remote code execution (RCE) with a CVSS v3.1 base score of 8.1 (High severity). This vulnerability can lead to complete compromise of the affected system's confidentiality and integrity, as attackers gain the ability to execute arbitrary commands. The primary risk lies in supply chain attacks, where malicious pickle files can be distributed through legitimate channels, infecting numerous downstream users. PyTorch models, often distributed as pickle files, are particularly vulnerable, meaning that compromised models could propagate malware to researchers, developers, and production systems globally, leading to widespread data theft, system sabotage, or further network penetration.

Recommendation

• Immediately update picklescan to version 0.0.30 or later to remediate CVE-2025-71342.
• Implement strict validation and sandboxing for deserialization of Python pickle files, especially those from untrusted or external sources.
• Educate development teams on the risks associated with deserializing untrusted data, specifically in the context of CVE-2025-71342 and pickle.load.
• Review existing practices for handling and loading machine learning models (e.g., PyTorch models) to ensure only verified and scanned pickle files are processed.