CVE-2024-3568 — CRITICAL (CVSS 9.6) AI Security Vulnerability

CISO Take

Any team loading TensorFlow checkpoints with HuggingFace Transformers is exposed to remote code execution — a single malicious checkpoint silently compromises the training machine. Patch immediately and enforce SafeTensors-only checkpoint loading. Audit all checkpoint sources: model hubs, shared drives, and third-party-provided weights are all viable delivery vectors.

Risk Assessment

CVSS 9.6 Critical with Changed scope (S:C) — exploitation grants full system compromise beyond the ML process boundary. Low attack complexity and no privileges required makes this trivially weaponizable once a malicious checkpoint is staged. User interaction is the only friction, but loading external checkpoints is standard ML practice, so social engineering is minimal. Exposure is highest in organizations running fine-tuning, transfer learning, or experiment resumption workflows that pull weights from community hubs or shared repositories.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
transformers	pip	—	No patch
160.2K OpenSSF 4.9 7.8K dependents Pushed 7d ago 39% patched ~101d to patch Full package profile →

Do you use transformers? You're affected.

Severity & Risk

CVSS 3.1

9.6 / 10

EPSS

24.4%

chance of exploitation in 30 days

Higher than 96% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ Public PoC indexed (trickest/cve)

○ EPSS exploit prediction: 24%

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Network

AC Low

PR None

UI Required

S Changed

C High

I High

A High

Recommended Action

6 steps

Patch: Upgrade to transformers version containing commit 693667b8ac8138b83f8adb6522ddaf42fa07c125.
Migrate to SafeTensors: Enforce use_safetensors=True for all checkpoint loading — SafeTensors eliminates pickle deserialization entirely.
Allowlist checkpoint sources: Restrict load_repo_checkpoint() calls to internal, hash-verified artifact stores. Reject externally sourced .ckpt or .bin files without integrity verification.
Sandbox training jobs: Run training workloads in isolated containers or VMs with no access to production credentials or internal networks.
Detection: Alert on unexpected subprocess spawns, outbound network connections, or file writes outside the working directory during model loading.
Audit: Inventory all training scripts calling load_repo_checkpoint() and review checkpoint provenance.

CISA SSVC Assessment

Decision Track

Exploitation none

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

Code Execution Supply Chain Framework Model AML.T0010.003 - Model AML.T0011.000 - Unsafe AI Artifacts AML.T0018.002 - Embed Malware AML.T0058 - Publish Poisoned Models AML.T0072 - Reverse Shell

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity Article 9 - Risk management system

ISO 42001

A.10.1 - AI system security A.6.2.3 - AI supply chain security

NIST AI RMF

GOVERN 6.2 - AI risk in the supply chain MANAGE 2.2 - Risk treatment for identified AI risks

OWASP LLM Top 10

LLM05:2025 - Improper Output Handling / Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2024-3568?

Any team loading TensorFlow checkpoints with HuggingFace Transformers is exposed to remote code execution — a single malicious checkpoint silently compromises the training machine. Patch immediately and enforce SafeTensors-only checkpoint loading. Audit all checkpoint sources: model hubs, shared drives, and third-party-provided weights are all viable delivery vectors.

Is CVE-2024-3568 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-3568, increasing the risk of exploitation.

How to fix CVE-2024-3568?

1. Patch: Upgrade to transformers version containing commit 693667b8ac8138b83f8adb6522ddaf42fa07c125. 2. Migrate to SafeTensors: Enforce `use_safetensors=True` for all checkpoint loading — SafeTensors eliminates pickle deserialization entirely. 3. Allowlist checkpoint sources: Restrict `load_repo_checkpoint()` calls to internal, hash-verified artifact stores. Reject externally sourced `.ckpt` or `.bin` files without integrity verification. 4. Sandbox training jobs: Run training workloads in isolated containers or VMs with no access to production credentials or internal networks. 5. Detection: Alert on unexpected subprocess spawns, outbound network connections, or file writes outside the working directory during model loading. 6. Audit: Inventory all training scripts calling `load_repo_checkpoint()` and review checkpoint provenance.

What systems are affected by CVE-2024-3568?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model fine-tuning workflows, MLOps pipelines, transfer learning infrastructure, model serving (checkpoint loading at startup).

What is the CVSS score for CVE-2024-3568?

CVE-2024-3568 has a CVSS v3.1 base score of 9.6 (CRITICAL). The EPSS exploitation probability is 24.43%.

Technical Details

NVD Description

The huggingface/transformers library is vulnerable to arbitrary code execution through deserialization of untrusted data within the `load_repo_checkpoint()` function of the `TFPreTrainedModel()` class. Attackers can execute arbitrary code and commands by crafting a malicious serialized payload, exploiting the use of `pickle.load()` on data from potentially untrusted sources. This vulnerability allows for remote code execution (RCE) by deceiving victims into loading a seemingly harmless checkpoint during a normal training process, thereby enabling attackers to execute arbitrary code on the targeted machine.

Exploitation Scenario

An adversary crafts a malicious TensorFlow checkpoint that embeds a Python pickle payload executing a reverse shell or credential harvester. They publish it to HuggingFace Hub under a convincing model name (e.g., 'bert-base-uncased-finetuned-v2') or share it directly with the target team as a pretrained baseline. A data scientist resumes training or runs a fine-tuning script that calls `load_repo_checkpoint()` — the moment the checkpoint loads, the pickle payload executes with the training process's privileges, establishing attacker control over the GPU workstation or cloud training node, exfiltrating AWS/GCP credentials from the environment, and potentially pivoting to the ML platform's model registry or data lake.