FoolishScan-

Foolish Scan v2.3 Gold Master Context-Aware CTF & Lab Re...

Integrating Multi-Agent Simulation, Behavioral Forensics, and Trust-Aware Machine Learning for Adaptive Insider Threat Detection

We present a hybrid framework for adaptive insider-threat detection that tightly integrates multi-agent simulation MAS, layered Security Information and Event Management SIEM correlation, behavioral and communication forensics, trust-aware machine learning, and Theory-of-Mind ToM reasoning...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

CVEhunter-Tools

CVEhunter: Integrated AI-Assisted Code Auditing Toolkit Windo...

Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways

The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tuning or auxiliary decoders,...

EUVD-2023-60296

In the Linux kernel, the following vulnerability has been resolved: net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses When using the felix driver the only one which supports UC filtering and MC filtering as a DSA master for a random other DSA switch, one can see the followi...

CVE-2023-54149

In the Linux kernel, the following vulnerability has been resolved: net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses When using the felix driver the only one which supports UC filtering and MC filtering as a DSA master for a random other DSA switch, one can see the followi...

UBUNTU-CVE-2023-54149

In the Linux kernel, the following vulnerability has been resolved: net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses When using the felix driver the only one which supports UC filtering and MC filtering as a DSA master for a random other DSA switch, one can see the followi...

CVE-2023-54149 net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses

In the Linux kernel, the following vulnerability has been resolved: net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses When using the felix driver the only one which supports UC filtering and MC filtering as a DSA master for a random other DSA switch, one can see the followi...

CVE-2023-54149 net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses

In the Linux kernel, the following vulnerability has been resolved: net: dsa: avoid suspicious RCU usage for synced VLAN-aware MAC addresses When using the felix driver the only one which supports UC filtering and MC filtering as a DSA master for a random other DSA switch, one can see the followi...

CVE-2023-54149

CVE-2023-54149 affects the Linux kernel where, when the felix driver is used as a DSA master for a VLAN-aware bridge, vlan_for_each() is invoked from a context that may not hold rtnl_lock, leading to a potentially unsafe RCU read-side usage. The advisory notes that vlan_for_each() expects rtnl_lo...

Failure Analysis of Safety Controllers in Autonomous Vehicles under Object-Based LiDAR Attacks

Autonomous vehicles rely on LiDAR based perception to support safety critical control functions such as adaptive cruise control and automatic emergency braking. While previous research has shown that LiDAR perception can be manipulated through object based spoofing and injection attacks, the impa...

Energy-Efficient Multi-LLM Reasoning for Binary-Free Zero-Day Detection in IoT Firmware

Securing Internet of Things IoT firmware remains difficult due to proprietary binaries, stripped symbols, heterogeneous architectures, and limited access to executable code. Existing analysis methods, such as static analysis, symbolic execution, and fuzzing, depend on binary visibility and...

Cyber Threat Detection Enabled by Quantum Computing

Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware, yet even strong classical systems still miss rare or borderline attacks when the data distribution drifts. Small, near-term quantum processors are now available, but existing...

BGPFuzz: Automated Configuration Fuzzing of the Border Gateway Protocol

Telecommunications networks rely on configurations to define routing behavior, especially in the Border Gateway Protocol BGP, where misconfigurations can lead to severe outages and security breaches, as demonstrated by the 2021 Facebook outage. Unlike existing approaches that rely on synthesis or...

injection-research

injection-research A study comparing injection vulnerabilities...

TopoReformer: Mitigating Adversarial Attacks Using Topological Purification in OCR Models

Adversarially perturbed images of text can cause sophisticated OCR systems to produce misleading or incorrect transcriptions from seemingly invisible changes to humans. Some of these perturbations even survive physical capture, posing security risks to high-stakes applications such as document...

Resilient Distribution Network Planning against Dynamic Malicious Power Injection Attacks

Active distribution networks facilitating bidirectional power exchange with renewable energy resources are susceptible to cyberattacks due to integration of a diverse array of cyber components. This study introduces a grid-level defense strategy aimed at enhancing attack resiliency based on...

VULPO: Context-Aware Vulnerability Detection Via On-Policy LLM Optimization

The widespread reliance on open-source software dramatically increases the risk of vulnerability exploitation, underscoring the need for effective and scalable vulnerability detection VD. Existing VD techniques, whether traditional machine learning-based or LLM-based approaches like prompt...

NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks

Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To...