vulnerability / NVI-00013171 / Revision 2
Picklescan is vulnerable to RCE through missing detection when calling numpy.f2py.crackfortran.myeval
GitHub lists this reviewed advisory for picklescan. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.
This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.
01 / Meaning
Why it matters
Published affected context helps an owner decide what evidence to gather. This report has not examined your application.
8.1 high
GitHub source severity; exploitability and local consequence require separate evidence.
As of · GitHub Advisory Database
8.1 HIGH (3.1)
disclosure@vulncheck.com severity; no combined Null risk score.
7.6 HIGH (4.0)
disclosure@vulncheck.com severity; no combined Null risk score.
02 / Applicability
The affected context
Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.
picklescan
source listed< 0.0.33
### Summary Picklescan uses numpy.f2py.crackfortran.myeval, which is a function in numpy to execute remote pickle files. ### Details The attack payload executes in the following steps: - First, the attacker crafts the payload by calling the numpy.f2py.crackfortran.myeval function in its reduce method - Then, when the victim checks whether the pickle file is safe by using the Picklescan library and this library doesn't detect any dangerous functions, they decide to use pickle.load() on this malicious pickle file, thus leading to remote code execution. ### PoC ``` class RCE: def __reduce__(self): from numpy.f2py.crackfortran import myeval return (myeval, ("os.system('ls')",)) ``` ### Impact Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects. ### Report by Pinji Chen (cpj24@mails.tsinghua.edu.cn) from the NISL lab (https://netsec.ccert.edu.cn/about) at Tsinghua University, Guanheng Liu (coolw
03 / Response
What to do next
- investigate
Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.
Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system. NIST National Vulnerability Database
A fixed release is not deployment evidence.
Check current vendor support and compatibility. After making a change, verify the deployed build and relevant runtime conditions. Keep the evidence, owner and time. An updated lockfile alone does not prove what is running.
04 / Limits
What this report cannot establish
- Customer inventory, deployment, reachability and exploitation have not been inspected.
- NVD lastModified does not establish freshness of every associated CPE record.
05 / Provenance
Sources you can inspect
Each assertion retains its source. Mirrored records may repeat one underlying publisher; additional links are not independent corroboration.
- GHSA-3329-ghmp-jmv5 ↗
GitHub Advisory Database · vendor advisory
Observed . GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.
- CVE-2025-71365 ↗
NIST National Vulnerability Database · official catalog
Observed . This product uses data from the NVD API but is not endorsed or certified by the NVD.
Public revision history
Only published revisions appear here. Routine fetches do not create a material-change event.
- Revision 2 ·
Source facts changed; compare the preserved prior revision.
- Revision 1 ·
First indexed by Null; source disclosure date is shown separately.