checkov

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kin

A new foundation for code. Kin is a graph-native code repository for people and AI agents. It stores source, recorded code relationships, and versioned history as repository state. The graph is the repository model, not a search index maintained beside another repository. Functions, types, and th...

LCSAJdump

LCSAJdump Framework universal basado en grafos para el descubrimiento automatizado de gadgets...

apparatus

ASTo - Herramienta de Software para Aparatos Aviso de archivo - Tras la finalización de mi grado de investigación, el repositorio ha sido archivado. ASTo ha tenido un impacto significativo en mi investigación. Quiero expresar mi gratitud a todos los que ayudaron a mejorarlo. Sin embargo, esto no...

grapl

Grapl Grapl is a graph-based SIEM platform built by-and-for incident response engineers. NOTICE Grapl has ceased operations as a company. As such, this code is no longer being actively developed, but will remain available in an archived state. Details Grapl leverages graph data structures at its...

[SECURITY] Fedora 45 Update: gegl04-0.4.70-5.fc45

GEGL Generic Graphics Library is a graph based image processing framework. GEGLs original design was made to scratch GIMP's itches for a new compositing and processing core. This core is being designed to have minimal dependencies and a simple well defined API...

[SECURITY] Fedora 44 Update: gegl04-0.4.70-5.fc44

GEGL Generic Graphics Library is a graph based image processing framework. GEGLs original design was made to scratch GIMP's itches for a new compositing and processing core. This core is being designed to have minimal dependencies and a simple well defined API...

kin v0.6.4

A new foundation for code. Kin is a graph-native code repository for people and AI agents. It stores source, recorded code relationships, and versioned history as repository state. The graph is the repository model, not a search index maintained beside another repository. Functions, types, and th...

CVE-2026-59245 Apache Airflow FAB provider: FAB auth manager: a DAG named "DAGs" hijacks the global all-DAGs permission (access_control privilege escalation via resource_name() collision)

In the Apache Airflow FAB auth manager, a DAG whose dagid is DAGs collided with the global all-DAGs permission resource name produced by resourcename, so a user granted per-DAG accesscontrol on that one DAG was silently granted the global all-DAGs permission privilege escalation. The escalation...

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

Large Language Models LLMs are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks to the integrity and...

PySpector 安全漏洞

PySpector is a high-performance Python static security analysis framework based on graphs, developed by Tommaso Bona. Versions of PySpector prior to 0.1.8 contained security vulnerabilities. These vulnerabilities stemmed from an incomplete blacklist of plugin security validators, which could allo...

[SECURITY] Fedora 44 Update: gegl04-0.4.70-2.fc44

GEGL Generic Graphics Library is a graph based image processing framework. GEGLs original design was made to scratch GIMP's itches for a new compositing and processing core. This core is being designed to have minimal dependencies and a simple well defined API...

HAL -- an Open-Source Framework for Gate-Level Netlist Analysis

HAL is an open-source framework for gate-level netlist analysis, an integral step in hardware reverse engineering. It provides analysts with an interactive GUI, an extensible plugin system, and APIs in both C++ and Python for rapid prototyping and automation. In addition, HAL ships with plugins f...

Changing the physics of cyber defense

The Deputy CISO blog series is whereMicrosoft Deputy Chief Information Security Officers CISOs share their thoughts on what is most important in their respective domains. In this series, you will get practical advice, tactics to start and stop deploying, forward-looking commentary on where the...

NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...

State-Of-The-Art in Software Security Visualization: a Systematic Review

Software security visualization is an interdisciplinary field that combines the technical complexity of cybersecurity, including threat intelligence and compliance monitoring, with visual analytics, transforming complex security data into easily digestible visual formats. As software systems get...

Automated Cyber Defense with Generalizable Graph-Based Reinforcement Learning Agents

Deep reinforcement learning RL is emerging as a viable strategy for automated cyber defense ACD. The traditional RL approach represents networks as a list of computers in various states of safety or threat. Unfortunately, these models are forced to overfit to specific network topologies, renderin...

A Graph-Based Approach to Alert Contextualisation in Security Operations Centres

Interpreting the massive volume of security alerts is a significant challenge in Security Operations Centres SOCs. Effective contextualisation is important, enabling quick distinction between genuine threats and benign activity to prioritise what needs further analysis.This paper proposes a...

KillChainGraph: ML Framework for Predicting and Mapping ATT&CK Techniques

The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain...

Mitigating Distribution Shift in Graph-Based Android Malware Classification Via Function Metadata and LLM Embeddings

Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets, yet we find they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family - a scenario where strong generalization would typically be expected. This...