CVE-2026-47476: Uncontrolled Resource Consumption

NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause uncontrolled resource consumption. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-47480: Uncaught Exception

NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause an uncaught exception. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-47482: Missing Release of Memory after Effective Lifetime

NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause missing release of memory after effective lifetime. A successful exploit of this vulnerability might lead to denial of service...

PT-2026-58758

NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause uncontrolled resource consumption. A successful exploit of this vulnerability might lead to denial of service...

PT-2026-58762

Name of the Vulnerable Software and Affected Versions NVIDIA Triton Inference Server for Linux affected versions not specified Description An issue exists where an attacker can trigger an uncaught exception. This flaw is network exploitable and requires no authentication, which may lead to a deni...

pharia-inference-sdk (0.1.0), solace-agent-mesh (>=1.8.0, <=1.28.4) potentially affected by CVE-2026-55865 via python-liquid (>=2.0.2, <=2.2.0)

python-liquid PYPI version =2.0.2, =1.8.0, =1.28.4 Source cves: CVE-2026-55865 Source advisory: OSV:PYSEC-2026-3035...

pharia-data-sdk (>=0.1.1, <=0.1.6), pharia-inference-sdk (>=0.1.2, <=0.1.6) +1 more potentially affected by CVE-2026-55865 via python-liquid (1.9.4)

python-liquid PYPI version 1.9.4 is affected by a known vulnerability. The following packages have a transitive dependency on python-liquid and may be impacted: - pharia-data-sdk =0.1.1, =0.1.2, =0.1.0, =0.1.7 Source cves: CVE-2026-55865 Source advisory: OSV:PYSEC-2026-3035...

vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels

SummaryAll temperature validation gates use comparison operators , which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that...

PYSEC-2026-3064 SGLang: Reachable Assertion via  lora_path  in LoRAManager enables remote Denial of Dervice

A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...

SGLang: Reachable Assertion via  lora_path  in LoRAManager enables remote Denial of Dervice

A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...

PYSEC-2026-3060 Amazon SageMaker Python SDK is missing integrity verification in its Triton inference handler

Summary Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. An issue exists where, under certain circumstances, the Triton inference handler deserializes model artifacts without performing integrity verification, allowing...

mlopspython-inference (>=67580760626172675.0.0, <=757069802391466481559610331145) potentially affected by CVE-2026-1462 via keras (2.12.0rc0)

keras PYPI version 2.12.0rc0 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - mlopspython-inference =67580760626172675.0.0, =757069802391466481559610331145 Source cves: CVE-2026-1462 Source advisory: OSV:PYSEC-2026-2547...

PT-2026-59684

A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/lora manager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lora path leads to reachable assertion. The attack can be launched...

Securing LLMs in the Wild: Privacy and Security Challenges at the Edge

Large Language Models LLMs are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deployments offer scalable compute, concerns over data sovereignty, compliance, latency, and third-party dependence are...

SoK: Federated Learning for Intrusion Detection in Vehicular Networks

Modern vehicular networks face an expanding attack surface across internal Electronic Control Units ECUs and external Vehicle-to-Everything V2X communication. Federated Learning FL has emerged as a decentralized paradigm to deploy Intrusion Detection Systems IDS without compromising data privacy...

blrec (>=1.8.0a3, <=2.0.0b5), dagster-looker (>=0.26.6rc0, <=0.29.21) +7 more potentially affected by CVE-2026-55865 via python-liquid (>=1.9.4, <=2.2.0)

python-liquid PYPI version =1.9.4, =1.8.0a3, =0.26.6rc0, =0.8.0, =0.1.1, =0.1.0, =0.1.0, =0.4.0, =0.0.1, =1.8.0, =1.28.4 Source cves: CVE-2026-55865 Source advisory: MEND:CVE-2026-55865...

Type confusion leading to segfault in Tensorflow

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion:pythonimport tensorflow as [email protected] test: y = tf.rawops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return ytestThe axis argument is...

PYSEC-2026-3211 Type confusion leading to segfault in Tensorflow

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: python import tensorflow as tf @tf.function def test: y = tf.rawops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return y test The axis argument...

Out of bounds read in Tensorflow

ImpactTensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK which is a no-op during production:ccif nodet.typeid != TFTUNSET int ix = inputidxi; DCHECKix nodet.argssize "input " i " should have an output " ix " but instead only has " nodet.argssize "...

PYSEC-2026-3249 Out of bounds read in Tensorflow

Impact TensorFlow's type inference can cause a heap OOB read as the bounds checking is done in a DCHECK which is a no-op during production: cc if nodet.typeid != TFTUNSET int ix = inputidxi; DCHECKix nodet.argssize "input " i " should have an output " ix " but instead only has " nodet.argssize "...