DEBIAN-CVE-2026-109261

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

UBUNTU-CVE-2026-109261

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261 ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 Memory Leak in GIF Encoder

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261

ImageMagick versions prior to 6.9.13-57 and 7.x before 7.1.2-32 are vulnerable to a memory leak in the GIF encoder . The root cause is that allocated memory is not released when color quantization fails during the encoding process. An attacker can trigger this by supplying specific images that ca...

CVE-2026-109261 ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 Memory Leak in GIF Encoder

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261 ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 Memory Leak in GIF Encoder

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

EUVD-2026-97705

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

CVE-2026-109261: Missing Release of Memory after Effective Lifetime

ImageMagick before 6.9.13-57 and 7.x before 7.1.2-32 contains a memory leak in the GIF encoder because allocated memory is not released when color quantization fails during encoding. Attackers can supply images whose GIF conversion causes quantization to fail, leaking memory with each attempt and...

PT-2026-110678

Name of the Vulnerable Software and Affected Versions ImageMagick versions prior to 6.9.13-57 ImageMagick versions prior to 7.1.2-32 Description A memory leak exists in the GIF encoder. This occurs because allocated memory is not released when color quantization—the process of reducing the number...

Improper Validation of Specified Quantity in Input

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Improper Validation of Specified Quantity in Input in allreducermsfusion.py when registering pattern replacements for mixed-dtype allreduce RMSNorm...

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

Large language models LLMs have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic...

Reliable Federated TinyML Deployment for IoT Security

The growing deployment of Internet of Things IoT devices has increased the need for privacy-preserving intrusion detection systems that operate directly on resource-constrained hardware. Federated Learning enables collaborative model training without sharing raw data, but conventional federated...

PT-2026-95545

Name of the Vulnerable Software and Affected Versions LMDeploy versions 0.12.1 through 0.12.2 Description Code injection is possible when loading a malicious HuggingFace model. The issue occurs because the quant dtype value from the model's quantization config is passed to the eval function in...

AGENTQ: Quantization-Conditioned Backdoor Attacks on LLM Agents

Quantization is one of the default deployment paths for open-weight LLM agents, but it is not behavior-preserving: an adversary can release a full-precision checkpoint that passes audits yet misbehaves once quantized, termed as quantization-conditioned attack QCA. Prior QCA work targets free-text...

Variational Probabilistic Quantization for Secret Key Generation

Secret key generation from correlated observations at Alice and Bob, in the presence of an eavesdropper Eve, underpins physical-layer security. Classical pipelines quantize by hand, amplify privacy afterwards, and optimize no objective tied to a key rate. We propose Variational Probabilistic...

Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability

Quantized Vision-Language-Action VLA models expose a weight-fault surface: Rowhammer-style faults can corrupt deployed INT8 bits. We present the first bit-flip attack on a VLA: a few gradient-selected flips reduce closed-loop success to $0%$, while hundreds of random flips are harmless. Across...

Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron MLP mode...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

ImpactWhen tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=1,1, dtype=tf.float32,...