Researchers Uncover Flaws in Popular Open
Vulnerabilities mentionedAll →
| CVE | Vulnerability | CVSS | EPSS | Flags | Affected | Exposure | Published |
|---|---|---|---|---|---|---|---|
| CVE-2023-5245 | FileUtil.extract() enumerates all zip file entries and extracts each file without validating whether file paths in the archive are outside the intended director FileUtil.extract() enumerates all zip file entries and extracts each file without validating whether file paths in the archive are outside the intended directory. When creating an instance of TensorflowModel using the saved_model format and an exported tensorflow model, the apply() function invokes the vulnerable implementation of FileUtil.extract(). Arbitrary file creation can directly lead to code execution NVD description · AI analysis pending | 9.8 | 1% | PoC |
| — | |
| CVE-2024-27132 | Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. This issue leads to a client-side RCE when running an untrusted recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over template variables. NVD description · AI analysis pending | 9.6 | <1% | PoC |
| — | |
| CVE-2024-6960 | The H2O machine learning platform uses "Iced" classes as the primary means of moving Java Objects around the cluster. The H2O machine learning platform uses "Iced" classes as the primary means of moving Java Objects around the cluster. The Iced format supports inclusion of serialized Java objects. When a model is deserialized, any class is allowed to be deserialized (no class whitelist). An attacker can construct a crafted Iced model that uses Java gadgets and leads to arbitrary code execution when imported to the H2O platform. NVD description · AI analysis pending | 7.5 | <1% | — | — |
Full article438 words · extracted from thehackernews.com · click to collapse
Ravie LakshmananDec 06, 2024Artificial Intelligence / Vulnerability
Cybersecurity researchers have disclosed multiple security flaws impacting open-source machine learning (ML) tools and frameworks such as MLflow, H2O, PyTorch, and MLeap that could pave the way for code execution.
The vulnerabilities, discovered by JFrog, are part of a broader collection of 22 security shortcomings the supply chain security company first disclosed last month.
Unlike the first set that involved flaws on the server-side, the newly detailed ones allow exploitation of ML clients and reside in libraries that handle safe model formats like Safetensors.
"Hijacking an ML client in an organization can allow the attackers to perform extensive lateral movement within the organization," the company said. "An ML client is very likely to have access to important ML services such as ML Model Registries or MLOps Pipelines."
This, in turn, could expose sensitive information such as model registry credentials, effectively permitting a malicious actor to backdoor stored ML models or achieve code execution.
The list of vulnerabilities is below -
- CVE-2024-27132 (CVSS score: 7.2) - An insufficient sanitization issue in MLflow that leads to a cross-site scripting (XSS) attack when running an untrusted recipe in a Jupyter Notebook, ultimately resulting in client-side remote code execution (RCE)
- CVE-2024-6960 (CVSS score: 7.5) - An unsafe deserialization issue in H20 when importing an untrusted ML model, potentially resulting in RCE
- A path traversal issue in PyTorch's TorchScript feature that could result in denial-of-service (DoS) or code execution due to arbitrary file overwrite, which could then be used to overwrite critical system files or a legitimate pickle file (No CVE identifier)
- CVE-2023-5245 (CVSS score: 7.5) - A path traversal issue in MLeap when loading a saved model in zipped format can lead to a Zip Slip vulnerability, resulting in arbitrary file overwrite and potential code execution
JFrog noted that ML models shouldn't be blindly loaded even in cases where they are loaded from a safe type, such as Safetensors, as they have the capability to achieve arbitrary code execution.
"AI and Machine Learning (ML) tools hold immense potential for innovation, but can also open the door for attackers to cause widespread damage to any organization," Shachar Menashe, JFrog's VP of Security Research, said in a statement.
"To safeguard against these threats, it's important to know which models you're using and never load untrusted ML models even from a 'safe' ML repository. Doing so can lead to remote code execution in some scenarios, causing extensive harm to your organization."
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Text extracted automatically; images, tables and formatting may be missing. Original: https://thehackernews.com/2024/12/researchers-uncover-flaws-in-popular.html