SUSE CVE-2026-5757

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

FlipGuard: Defending Large Language Models against Quantization-Conditioned Backdoor Attacks

Model quantization is essential for the efficient deployment of Large Language Models LLMs, but introduces a critical vulnerability: Quantization-Conditioned Backdoor QCB attacks. In these attacks, malicious behaviors remain dormant in full-precision models and activate only after specific...

Use After Free

Overview Affected versions of this package are vulnerable to Use After Free via the model quantization engine. An attacker can access and extract sensitive data from the server's heap memory by sending unauthenticated remote requests. Remediation There is no fixed version for...

CVE-2026-5757

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

CVE-2026-5757 There exists an unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

CVE-2026-5757

CVE-2026-5757 concerns Ollama’s model quantization engine. The CERT entry describes an unauthenticated remote information-disclosure vulnerability triggered via the model upload interface. Root cause: three factors—no bounds checking on user-supplied GGUF header metadata, unsafe memory access usi...

CVE-2026-5757 There exists an unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

EUVD-2026-39786

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

CVE-2026-5757

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

CVE-2026-5757 There exists an unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

CVE-2026-5757: Out-of-bounds Read

Unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine allows an attacker to read and exfiltrate the server's heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence...

vLLM: GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving

Summary Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels csrc/quantization/gguf/ggufkernel.cu causes partial tensor processing. The output tensor is allocated at full size via torch::empty uninitialized memory, but the dequantize CUDA kernel processes only a truncated...

libvpx:vpx_enc_fuzzer_vp8_nalloc: Use-of-uninitialized-value in vp8_regular_quantize_b_sse4_1

Project: https://chromium.googlesource.com/webm/libvpx Detailed Report: https://oss-fuzz.com/testcase?key=4581817557778432 Project: libvpx Fuzzing Engine: libFuzzer Fuzz Target: vpxencfuzzervp8nalloc Job Type: libfuzzermsanlibvpx Platform Id: linux Crash Type: Use-of-uninitialized-value Crash...

Widening the Gap: Exploiting LLM Quantization Via Outlier Injection

LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risks: an adversary may release a model that appears benign in full precision but exhibits malicious behavior once quantized by users. However, existin...

Exploit for CVE-2026-7482

CVE-2026-7482: Ollama Heap Out-of-Bounds Read 1-Day PoC Thi...

CVE-2026-7482: Out-of-bounds Read

Ollama before 0.17.1 contains a heap out-of-bounds read vulnerability in the GGUF model loader. The /api/create endpoint accepts an attacker-supplied GGUF file in which the declared tensor offset and size exceed the file's actual length; during quantization in fs/ggml/gguf.go and...

PT-2026-34454

Name of the Vulnerable Software and Affected Versions Ollama affected versions not specified Description An out-of-bounds memory read and write issue exists in the GGUF GPT-Generated Unified Format quantization engine. This occurs because the engine lacks proper bounds checking and trusts tensor...

Ollama GGUF Quantization Remote Memory Leak

Overview Ollama’s model quantization engine contains a vulnerability that allows an attacker with access to the model upload interface to read and potentially exfiltrate heap memory from the server. This issue may lead to unintended behavior, including unauthorized access to sensitive data and, i...

CVE-2025-33247

NVIDIA Megatron LM contains a vulnerability in quantization configuration loading, which could allow remote code execution. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering...

CVE-2026-24141

NVIDIA Model Optimizer for Windows and Linux contains a vulnerability in the ONNX quantization feature, where a user could cause unsafe deserialization by providing a specially crafted input file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges,...