PT-2026-52617

Name of the Vulnerable Software and Affected Versions picklescan versions prior to 0.0.30 Description The software fails to detect malicious pickle files that invoke idlelib.pyshell.ModifiedInterpreter.runcode within reduce methods. This allows attackers to embed undetected code in pickle files...

PT-2025-36709

Summary Using code.InteractiveInterpreter.runcode, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to code.InteractiveInterpreter.runcode function in reduce meth...

PT-2025-36696

Summary Using idlelib.pyshell.ModifiedInterpreter.runcode function, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to idlelib.pyshell.ModifiedInterpreter.runcod...

PT-2025-36717

Summary Using profile.Profile.run, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to profile.Profile.run function in reduce method Then when the victim after...

PT-2025-36718

Summary Using ensurepip. run pip function, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to ensurepip. run pip function in reduce method Then when the victim...

Tricking LLM-Based NPCs into Spilling Secrets

Large Language Models LLMs are increasingly used to generate dynamic dialogue for game NPCs. However, their integration raises new security concerns. In this study, we examine whether adversarial prompt injection can cause LLM-based NPCs to reveal hidden background secrets that are meant to remai...

MalLoc: toward Fine-Grained Android Malicious Payload Localization Via LLMs

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications apps. Traditional detection techniques often struggle to keep pace with emerging malware variants that employ advanced tactics such as code obfuscation and dynamic behavior...

A Systematic Approach to Predict the Impact of Cybersecurity Vulnerabilities Using LLMs

Vulnerability databases, such as the National Vulnerability Database NVD, offer detailed descriptions of Common Vulnerabilities and Exposures CVEs, but often lack information on their real-world impact, such as the tactics, techniques, and procedures TTPs that adversaries may use to exploit the...

CVE-2025-9392 Linksys RE6250/RE6300/RE6350/RE6500/RE7000/RE9000 qosClassifier stack-based overflow

A security vulnerability has been detected in Linksys RE6250, RE6300, RE6350, RE6500, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. This affects the function qosClassifier of the file /goform/qosClassifier. Such manipulation of the argument...

Rethinking Denial-Of-Service: a Conditional Taxonomy Unifying Availability and Sustainability Threats

This paper proposes a unified, condition-based framework for classifying both legacy and cloud-era denial-of-service DoS attacks. The framework comprises three interrelated models: a formal conditional tree taxonomy, a hierarchical lattice structure based on order theory, and a conceptual Venn...

Linksys多款产品 安全漏洞

Linksys RE6250 and others are a wireless extender from Linksys USA. A security vulnerability exists in various Linksys products, which stems from the incorrect operation of the parameters...

CVE-2025-9363 Linksys RE6250/RE6300/RE6350/RE6500/RE7000/RE9000 portTriggerManageRule stack-based overflow

A vulnerability has been found in Linksys RE6250, RE6300, RE6350, RE6500, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. This affects the function portTriggerManageRule of the file /goform/portTriggerManageRule. The manipulation of the argument triggerRuleName/schedule...

CVE-2025-9360

A security vulnerability has been detected in Linksys RE6250, RE6300, RE6350, RE6500, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. Impacted is the function accessControlAdd of the file /goform/accessControlAdd. Such manipulation of the argument ruleName/schedule lead...

Linksys多款产品 安全漏洞

Linksys RE6300 and others are products of Linksys, Inc.Linksys RE6300 is a wireless network signal extender.Linksys RE6250 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless...

CVE-2025-9355

A vulnerability was found in Linksys RE6250, RE6300, RE6350, RE6500, RE7000 and RE9000 1.0.013.001/1.0.04.001/1.0.04.002/1.1.05.003/1.2.07.001. Affected by this vulnerability is the function scheduleAdd of the file /goform/scheduleAdd. Performing manipulation of the argument ruleName results in...

GHSA-VV6J-3G6G-2PVJ Picklescan is missing detection when calling pytorch function torch.utils._config_module.load_config

Summary Using torch.utils.configmodule.loadconfig function, which is a pytorch library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker crafts the payload by calling the torch.utils.configmodule.loadconfig function via the...

Picklescan missing detection when calling pytorch function torch.jit.unsupported_tensor_ops.execWrapper

Summary Using torch.jit.unsupportedtensorops.execWrapper function, which is a pytorch library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.jit.unsupportedtensorops.execWrapper function...

Qilin Ransomware Gang Claims 4TB Data Breach at Nissan CBI

Qilin ransomware claims a 4TB data breach at Nissan CBI, leaking car design files, financial data, 3D models,…...

Comprehensive MCP Security Checklist: Protecting Your AI-Powered Infrastructure

With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models LLMs and Multi-Component Protocols MCP - bring immense potential, but also novel vulnerabilities that traditional...

Towards Scalable and Interpretable Mobile App Risk Analysis Via Large Language Models

Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis by security professionals aided by semi-automated tools. To address this inefficiency, we propose Mars, a system that...