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Running a PyTorch Model Can Now Give Hackers Full Control

The PickleScan 0-Day — CyberDudeBivash ThreatWire Breakdown

A critical new vulnerability — now being called thePickleScan 0-Day — exposes a devastating flaw in how PyTorch loads machine-learning models, allowing attackers to execute arbitrary system commands the moment a victim loads a malicious .pt or .pth file.

This exploit abuses Python’s pickle deserialization, turning every AI model into a potential remote-code-execution payload.
In short:

“Download a PyTorch model → Run torch.load() → Attacker gets full system control.”

This is the most dangerous, publicly weaponizable ML-ecosystem flaw we’ve seen since supply-chain poisoning attacks like Dependency Confusion and NPM Typosquatting.

CyberDudeBivash ThreatWire breaks down the risk, how attackers weaponize it, and what organizations must do immediately.

1. Why This Exploit Is So Dangerous

PyTorch uses Python pickle serialization to save and load model weights and architectures:

torch.load("model.pth")

But Python’s pickle format allows arbitrary code execution by design.

This means:

• Opening a malicious PyTorch model
• Running a simple .load()
• Importing a poisoned package
• Loading a pretrained layer from an unknown repo

…can instantly run:

• Reverse shells
• Credential stealers
• Backdoor implants
• System commands
• Cryptominers
• Lateral movement payloads

No warnings. No prompts. No signatures.

AI researchers, ML engineers, and data scientists are at extreme risk because they frequently download:

• “Pretrained models”
• Research reproductions
• Experiment checkpoints
• Kaggle shared models
• HuggingFace community uploads
• GitHub model weights
• PyTorch Hub auto-downloads

Attackers know this ecosystem is casual, trusted, and full of “black-box unknown model files.”

2. How the PickleScan 0-Day Works

The vulnerability leverages three weaknesses:

A. Pickle allows arbitrary code execution

When deserializing:

pickle.load()

It can invoke arbitrary Python objects via __reduce__().
Attackers embed system-level commands inside model files.

B. PyTorch automatically trusts model weights

PyTorch simply calls pickle internally:

def load(f):
    return pickle.load(f)

Zero validation.
Zero sandboxing.
Zero warnings.

C. Attackers can hide payloads inside:

• state_dict()
• Custom layers
• Optimizer states
• Checkpoint metadata
• Generators / Discriminators
• Hidden residual blocks
• Even “unused” tensors

These payloads execute BEFORE the model is even used.

3. How Hackers Are Weaponizing It (Real-World Exploit Patterns)

CyberDudeBivash ThreatWire observed the following patterns:

1. Backdoored HuggingFace model uploads

Fake research models containing:

• reverse shells
• token exfiltration
• credential dumpers

2. Malicious “training checkpoint” repos

Attackers fork popular repos, upload a malicious checkpoint, rename it model_final.pth.

Researchers run:
torch.load("model_final.pth") → compromised.

3. Compromised Kaggle notebooks

Auto-downloads from:
wget <malicious URL>/model.pth

Students, researchers, and ML hobbyists are high-risk.

4. Supply-chain poisoning of Torch Hub

Attackers upload “pretrained community models” that PyTorch autoloads.

Just calling:

torch.hub.load("user/repo", "model")

executes malicious pickle code.

5. Enterprise compromise through ML pipelines

Organizations using MLOps (Kubeflow, MLflow, BentoML, Sagemaker, Vertex AI, Azure ML) risk:

• Agent compromise
• CI/CD takeover
• Artifact poisoning
• GPU node backdooring
• Credential harvesting
• Secret leaks

4. Impact on SOC, DFIR, and Enterprises

This is not a “developer vulnerability.”
It is an enterprise security vulnerability with massive consequences.

Attackers can:

✔ Gain remote command execution
✔ Install persistence
✔ Steal cloud keys from ~/.aws
✔ Exfiltrate training data
✔ Poison ML pipelines
✔ Shift model behavior
✔ Hijack credentials from GPU servers
✔ Compromise entire DevOps environments

High-risk groups include:

• AI research organizations
• LLM training labs
• Cloud ML deployments
• MLOps pipelines
• Data science teams
• Kaggle, HF, GitHub users
• Universities
• Defense & medical AI units

5. CyberDudeBivash Emergency Mitigation Guide

 1. DO NOT LOAD UNTRUSTED .pth OR .pt FILES

This is the #1 rule.

 2. Replace torch.load() with SAFE loaders

Use:

torch.load(..., weights_only=True)

Or:

state = torch.load(..., map_location="cpu", pickle_module=safe_pickle)

 3. Use PickleScan Before Loading Models

CyberDudeBivash recommends a pre-check:

pip install picklescan
picklescan model.pth

It scans for:

• RCE payloads
• Suspicious reduce functions
• Hidden imports
• Unknown classes

 4. Lock Down ML Pipelines

Enable:

• Artifact signing
• Model origin verification
• MLOps security gates
• Container sandboxing

 5. Use PyTorch SafeTensors Whenever Possible

SafeTensors is:

• zero-execution
• zero-side-effects
• memory-safe
• RCE-proof

 6. Block suspicious PyTorch Hub autoloads

Disable autoloading from untrusted repos.

 7. SOC / SIEM Detection Rules

Detect Python spawning OS commands:

process.name:python AND child_process.name:(bash OR sh OR powershell OR cmd)

 8. DFIR Recommendations

If compromise suspected:

• isolate machine
• extract .pth model
• inspect with PickleScan
• check Python module imports
• review ~/.cache/torch
• audit ~/.ssh & cloud creds
• wipe/rebuild the environment

6. How CyberDudeBivash Helps Enterprises Defend Against This Threat

 PyTorch Model Security Audit

We analyze all ML models used in pipelines.

 Supply-Chain Security for MLOps

Checkpoint validation + artifact security.

 SOC Detection Engineering

SIEM rules for PyTorch RCE exploitation.

 DFIR for AI/ML Environments

Forensic investigation of compromised GPU nodes.

 Red-Team Model Poisoning Simulation

Attack simulation showing how adversaries compromise ML environments.

7. Final Warning from CyberDudeBivash ThreatWire

This is the most critical AI-ecosystem vulnerability of 2025/26.
Because unlike typical RCE flaws, this exploit hides inside AI models that:

• teams trust
• enterprises share
• researchers download
• pipelines silently load

Loading a malicious model is equivalent to running malware, with no antivirus, no sandbox, and no protection.

Every enterprise using PyTorch must update policies immediately.

#CyberDudeBivash #ThreatWire #PyTorchSecurity #PickleScan #AIThreats #MachineLearningSecurity #ModelPoisoning #SupplyChainAttack #RCEVulnerability #MLOpsSecurity #DFIR #ZeroTrustAI

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