CVE-2026-36741

U-SPEED AC1200 Gigabit Wi-Fi Router Model: T18-21K V1.0 is vulnerable to Command Injection. The Network Time Protocol NTP configuration interface does not properly sanitize user-supplied input. An authenticated user with permission to configure NTP settings can inject arbitrary system commands...

ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents

Exploitation is not a binary event. It is a ladder of acquiring progressive capabilities, from executing a single buggy line of code to taking full control of the target. However, existing LLM security benchmarks treat a crash as exploitation success. That single binary outcome collapses the hard...

CVE-2026-31250

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its averagemodel.py model averaging tool. The script loads PyTorch checkpoint files epoch.pt for model averaging using torch.load without enabling the...

CVE-2026-31252

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its model loading component. The framework uses torch.load to load model weight files e.g., llm.pt, flow.pt, hift.pt without enabling the security-restrictive...

CVE-2026-44223 vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters

vLLM is an inference and serving engine for large language models LLMs. From 0.18.0 to before 0.20.0, the extracthiddenstates speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The...

Deserialization of Untrusted Data

Overview adversarial-robustness-toolbox is a Toolbox for adversarial machine learning. Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the model loading process. An attacker can execute arbitrary code by uploading a maliciously crafted model file to an...

Deserialization of Untrusted Data

Overview ludwig is a Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations. Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the model serving process. An attacker can execute arbitrary code on the system by...

EUVD-2026-29559

The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec function...

GHSA-G76P-4VG5-F4QH llm CLI tool contains a code injection vulnerability via `--functions` command-line argument

The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec function...

Ludwig framework is vulnerable to insecure deserialization in its model serving component

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

llm CLI tool contains a code injection vulnerability via `--functions` command-line argument

The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec function...

GHSA-XP5Q-5Q7G-Q26R Ludwig framework is vulnerable to insecure deserialization in its model serving component

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

EUVD-2026-29561

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

EUVD-2026-29555

The CosyVoice project thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e 2025-30-21 contains an insecure deserialization vulnerability CWE-502 in its model loading process. When loading model files .pt from a user-specified directory via the --modeldir argument, the code uses torch.load without...

EUVD-2026-29503

The loadmodel function in the neuralmagictraining.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f 2024-07-21 is vulnerable to insecure deserialization CWE-502. When a user provides a single model file path e.g., .pt or .pth via the --model command-line argumen...

Deserialization of Untrusted Data

Overview snorkel is an A system for quickly generating training data with weak supervision Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the Trainer.load function. An attacker can execute arbitrary code by supplying a maliciously crafted model file that ...

EUVD-2026-29501

The loadmodel function in the neuralmagictraining.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f 2024-07-21 allows arbitrary code execution. When a user supplies a directory path via the --model command-line argument, the function reads a module.py file from...

CVE-2026-31239

The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization CWE-502 when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.frompretrained method uses torch.load to load the pytorchmodel.bin weight file without enabling the security-restrictive...

CVE-2026-31238

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization CWE-502 in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load without enabling the security-restrictive weightsonly=True...

CVE-2026-31229

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...