GHSA-wccx-j62j-r448 — HIGH AI Security Vulnerability

Q: Is GHSA-wccx-j62j-r448 actively exploited?

No confirmed active exploitation of GHSA-wccx-j62j-r448 has been reported, but organizations should still patch proactively.

Q: How to fix GHSA-wccx-j62j-r448?

1. PATCH: Upgrade fickling to 0.1.9 immediately — pip install 'fickling>=0.1.9'. 2. VERIFY: Confirm patched version hooks pickle.loads and _pickle.loads by running fickling's regression test suite. 3. DEFENSE-IN-DEPTH: Do not rely solely on fickling; implement model provenance controls (cryptographic signatures, hashes, trusted registries). 4. SCAN HISTORY: Audit logs for model loads that occurred while fickling <= 0.1.8 was in use — treat any externally-sourced model as suspect. 5. DETECT: Alert on unexpected subprocess spawning or network calls during model load/deserialization operations. 6. BLOCK: Consider SafeTensors format as a pickle-free alternative for model serialization where possible.

Q: What systems are affected by GHSA-wccx-j62j-r448?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, MLOps CI/CD pipelines, model registries, data science notebooks.

Q: What is the CVSS score for GHSA-wccx-j62j-r448?

No CVSS score has been assigned yet.

CISO Take

If your ML pipeline uses fickling's always_check_safety() as a security control for model deserialization, it is broken — attackers can bypass it entirely by targeting pickle.loads or _pickle.loads entry points. Patch fickling to 0.1.9 immediately and treat any model loaded via fickling <= 0.1.8 as potentially compromised. Do not rely on fickling as a sole defense layer; add upstream model provenance controls.

Risk Assessment

HIGH. The vulnerability completely defeats the advertised security guarantee of fickling's global safety mode. Exploitability is trivial — an attacker simply uses pickle.loads instead of pickle.load. Impact is remote code execution with the privileges of the loading process. Exposure is significant in any ML/AI pipeline that loads external or third-party model files and relies on fickling as a safety gate. Organizations that implemented fickling specifically to mitigate pickle deserialization risk now have a false sense of security, which is arguably worse than no control at all.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
fickling	pip	<= 0.1.8	`0.1.9`
622 OpenSSF 7.9 57 dependents Pushed 2d ago 100% patched ~5d to patch Full package profile →

Do you use fickling? You're affected.

Severity & Risk

CVSS 3.1

N/A

EPSS

N/A

Exploitation Status

No known exploitation

Sophistication

Trivial

Recommended Action

6 steps

PATCH

Upgrade fickling to 0.1.9 immediately — pip install 'fickling>=0.1.9'.
VERIFY

Confirm patched version hooks pickle.loads and _pickle.loads by running fickling's regression test suite.
DEFENSE-IN-DEPTH: Do not rely solely on fickling; implement model provenance controls (cryptographic signatures, hashes, trusted registries).
SCAN HISTORY

Audit logs for model loads that occurred while fickling <= 0.1.8 was in use — treat any externally-sourced model as suspect.
DETECT

Alert on unexpected subprocess spawning or network calls during model load/deserialization operations.
BLOCK

Consider SafeTensors format as a pickle-free alternative for model serialization where possible.

Classification

Code Execution Supply Chain Auth Bypass Framework Model Inference AML.T0010.001 - AI Software AML.T0011.000 - Unsafe AI Artifacts AML.T0018.002 - Embed Malware AML.T0049 - Exploit Public-Facing Application AML.T0107 - Exploitation for Defense Evasion

Compliance Impact

This CVE is relevant to:

EU AI Act

Art.15 - Accuracy, Robustness and Cybersecurity

ISO 42001

A.6.1.4 - AI System Security Controls

NIST AI RMF

GOVERN-1.1 - Organizational policies for AI risk MANAGE-2.2 - Mechanisms to sustain AI risk management

OWASP LLM Top 10

LLM09 - Overreliance

Frequently Asked Questions

What is GHSA-wccx-j62j-r448?

If your ML pipeline uses fickling's always_check_safety() as a security control for model deserialization, it is broken — attackers can bypass it entirely by targeting pickle.loads or _pickle.loads entry points. Patch fickling to 0.1.9 immediately and treat any model loaded via fickling <= 0.1.8 as potentially compromised. Do not rely on fickling as a sole defense layer; add upstream model provenance controls.

Is GHSA-wccx-j62j-r448 actively exploited?

No confirmed active exploitation of GHSA-wccx-j62j-r448 has been reported, but organizations should still patch proactively.

How to fix GHSA-wccx-j62j-r448?

1. PATCH: Upgrade fickling to 0.1.9 immediately — pip install 'fickling>=0.1.9'. 2. VERIFY: Confirm patched version hooks pickle.loads and _pickle.loads by running fickling's regression test suite. 3. DEFENSE-IN-DEPTH: Do not rely solely on fickling; implement model provenance controls (cryptographic signatures, hashes, trusted registries). 4. SCAN HISTORY: Audit logs for model loads that occurred while fickling <= 0.1.8 was in use — treat any externally-sourced model as suspect. 5. DETECT: Alert on unexpected subprocess spawning or network calls during model load/deserialization operations. 6. BLOCK: Consider SafeTensors format as a pickle-free alternative for model serialization where possible.

What systems are affected by GHSA-wccx-j62j-r448?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, MLOps CI/CD pipelines, model registries, data science notebooks.

What is the CVSS score for GHSA-wccx-j62j-r448?

No CVSS score has been assigned yet.

Technical Details

NVD Description

# Assessment The missing pickle entrypoints `pickle.loads`, `_pickle.loads`, and `_pickle.load` were added to the hook https://github.com/trailofbits/fickling/commit/8c24c6edabceab156cfd41f4d70b650e1cdad1f7. # Original report ## Summary `fickling.always_check_safety()` does not hook all pickle entry points. `pickle.loads`, `_pickle.loads`, and `_pickle.load` remain unprotected, enabling malicious payload execution despite global safety mode being enabled. ## Affected versions `<= 0.1.8` (verified on current upstream HEAD as of 2026-03-03) ## Non-duplication check against published Fickling GHSAs No published advisory covers hook-coverage bypass in `run_hook()`. Existing advisories are blocklist/detection bypasses (runpy, pty, cProfile, marshal/types, builtins, network constructors, OBJ visibility, etc.), not runtime hook coverage parity. ## Root cause `run_hook()` patches only: - `pickle.load` - `pickle.Unpickler` - `_pickle.Unpickler` It does not patch: - `pickle.loads` - `_pickle.load` - `_pickle.loads` ## Reproduction (clean upstream) ```python import io, pickle, _pickle from unittest.mock import patch import fickling from fickling.exception import UnsafeFileError class Payload: def __reduce__(self): import subprocess return (subprocess.Popen, (['echo','BYPASS'],)) data = pickle.dumps(Payload()) fickling.always_check_safety() # Bypass path with patch('subprocess.Popen') as popen_mock: pickle.loads(data) print('bypass sink called?', popen_mock.called) # True # Control path is blocked with patch('subprocess.Popen') as popen_mock: try: pickle.load(io.BytesIO(data)) except UnsafeFileError: pass print('blocked sink called?', popen_mock.called) # False ``` Observed on vulnerable code: - `pickle.loads` executes payload - `pickle.load` is blocked ## Minimal patch diff ```diff --- a/fickling/hook.py +++ b/fickling/hook.py @@ def run_hook(): - pickle.load = loader.load + pickle.load = loader.load + _pickle.load = loader.load + pickle.loads = loader.loads + _pickle.loads = loader.loads ``` ## Validation after patch - `pickle.loads`, `_pickle.loads`, and `_pickle.load` all raise `UnsafeFileError` - sink not called in any path Regression tests added locally: - `test_run_hook_blocks_pickle_loads` - `test_run_hook_blocks__pickle_load_and_loads` in `test/test_security_regressions_20260303.py` ## Impact High-confidence runtime protection bypass for applications that trust `always_check_safety()` as global guard.

Exploitation Scenario

An adversary targets an ML platform that validates user-submitted PyTorch models using fickling.always_check_safety() before serving them. The adversary crafts a malicious .pt file embedding a pickle payload with a __reduce__ method that spawns a reverse shell or exfiltrates credentials. Because the payload is designed to be loaded via pickle.loads (the standard PyTorch load path), fickling's patched pickle.load hook is never triggered. The model passes validation, is deployed to the inference cluster, and executes arbitrary code with the service account's privileges on first inference request. The attack requires no authentication beyond standard model submission access.