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...

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...

CVE-2026-5757

A flaw was found in Ollama's model quantization engine. An unauthenticated remote attacker can exploit this vulnerability to read and exfiltrate the server's heap memory. This could lead to sensitive data exposure, further compromise of the system, and allow for stealthy persistence within the...

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...

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...

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...

TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Chip manufacturing is a complex process, and to achieve a faster time to market, an increasing number of untrusted third-party tools and designs from around the world are being utilized. The use of these untrusted third party intellectual properties IPs and tools increases the risk of adversaries...