CVE-2026-22609
Unsafe Import Bypass in Fickling Static Analyzer Enables RCE
Publication date: 2026-01-10
Last updated on: 2026-01-10
Assigner: GitHub, Inc.
Description
Description
CVSS Scores
EPSS Scores
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Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| trailofbits | fickling | to 0.1.7 (exc) |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-502 | The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid. |
| CWE-184 | The product implements a protection mechanism that relies on a list of inputs (or properties of inputs) that are not allowed by policy or otherwise require other action to neutralize before additional processing takes place, but the list is incomplete. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
This vulnerability in Fickling, a Python pickle static analyzer, arises because its unsafe_imports() method did not flag several high-risk Python modules as unsafe. These missing modules include ctypes, importlib, runpy, code, and multiprocessing, which can be used to execute arbitrary code. As a result, malicious pickle files importing these modules could bypass Fickling's static safety checks and execute arbitrary code during deserialization. This undermines the tool's purpose of detecting unsafe pickles. The issue was fixed in version 0.1.7 by adding these modules to the unsafe imports blocklist. [1]
How can this vulnerability impact me? :
The vulnerability allows attackers to craft malicious pickle payloads that import dangerous Python modules not flagged by Fickling's static analyzer. This enables arbitrary code execution upon deserialization, potentially compromising confidentiality, integrity, and availability of systems using Fickling for pickle safety analysis. Attackers can bypass security checks and execute harmful commands or code, leading to severe security breaches. [1]
How can this vulnerability be detected on my network or system? Can you suggest some commands?
This vulnerability can be detected by using the Fickling tool version 0.1.7 or later, which includes enhanced static analysis to flag unsafe imports in Python pickle files. Specifically, the tool's `unsafe_imports()` method now detects dangerous modules such as ctypes, importlib, runpy, code, and multiprocessing that can be used for arbitrary code execution. To detect malicious pickles, you can run Fickling's safety check on pickle files to identify unsafe imports. While no explicit command lines are provided, the detection involves analyzing pickle files with Fickling's static analyzer, which will flag pickles importing these high-risk modules as unsafe. Example test cases in the codebase simulate detection of unsafe imports by constructing pickle payloads importing these modules and asserting detection. Therefore, running Fickling's analysis on pickle data in your environment will help detect exploitation attempts. [1, 2]
What immediate steps should I take to mitigate this vulnerability?
The immediate mitigation step is to upgrade Fickling to version 0.1.7 or later, where this vulnerability has been patched by expanding the list of unsafe modules detected during pickle analysis. This update includes adding missing high-risk modules (importlib, code, multiprocessing, runpy, ctypes, pydoc, and others) to the blocklist, improving validation of module and attribute names, and enhancing detection logic to prevent bypasses. Using the patched version ensures that malicious pickles importing these modules are flagged as unsafe, preventing attackers from bypassing Fickling's static safety checks. Additionally, reviewing and applying the security updates from the relevant commits and releases is recommended to maintain protection against this and related vulnerabilities. [3, 4, 5, 6]
How does this vulnerability affect compliance with common standards and regulations (like GDPR, HIPAA)?:
The vulnerability allows attackers to execute arbitrary code during deserialization by bypassing Fickling's static safety checks. This can lead to unauthorized access, modification, or destruction of data, thereby compromising confidentiality, integrity, and availability. Such security breaches can result in non-compliance with standards and regulations like GDPR and HIPAA, which mandate protection of sensitive data and require organizations to implement adequate security controls to prevent unauthorized access and data breaches. [1]