AWE

AWE: Adaptive Agents for Dynamic Web Penetration Testing Akshat Singh Jaswal · Ashish Baghel Accepted at NDSS LAST-X 2026 Abstract Modern web applications are increasingly produced through AI-assisted development and rapid no-code deployment pipelines, widening the gap between accelerating softwa...

seclab-taskflow-agent

GitHub Security Lab Taskflow Agent The Security Lab Taskflow Agent is an MCP-enabled multi-Agent framework for declarative, YAML-driven agentic workflows. Built on top of the OpenAI Agents SDK, it uses Pydantic for grammar validation and Jinja2 for template rendering. Core Concepts The Taskflow...

DeepAVR: Deep Analysis Via Multi-Agent Collaboration for Automated Vulnerability Repair

Automated Vulnerability Repair AVR has emerged as a critical defense against software security flaws, aiming to triage and fix bugs autonomously. Early LLM-based approaches focused on patch generation under the assumption of perfect fault localization, while recent end-to-end methods attempt to...

RegoMender: A Multi-Agent Framework for Automated Rego Policy Repair

Policy-as-Code PaC has emerged as a foundational paradigm in cloud-native systems. However, repairing Rego policies remains challenging due to their strict syntax, declarative semantics, and specific verification requirements. Existing automated program repair APR methods are mainly designed for...

HydraPoT: A Configurable Multi-Agent Framework for Cost-Aware LLM-Assisted Honeypots

Large language models LLMs can improve honeypot realism by generating dynamic, context-aware responses, but powerful cloud models incur substantial cost and latency, while lightweight models may provide weaker deception. This forces practitioners to choose between operational efficiency and...

EviRepair: Evidence-Driven Agents for Reliable Vulnerability Repair

Recent advances in large language models LLMs have enabled agent-based approaches for repository-level automated vulnerability repair AVR, showing promising repair capabilities. However, an important issue has received limited attention: how can we ensure that the evidence generated during the...

From Investigation Failures to Reliable SOC Agents: Understanding and Improving LLM-Based Alert Triage

Security operations centers SOCs must triage large volumes of alerts, most of which are benign, while missed attacks can remain uninvestigated. Tool-using large language model LLM agents can retrieve evidence during triage, but it remains unclear how reasoning strategies determine what to gather...

One Pipeline Does Not Fit All: TAILOR, a Type- and State-Aware Framework for CVE Reproduction

Growing vulnerability disclosure and widespread software reuse increase security teams' need for reproducible evidence to diagnose vulnerabilities, validate patches, and build regression tests. Producing such evidence at scale requires automated end-to-end CVE reproduction. Existing methods...

Autonomous AI Agents Compromise Thousands of Credentials in Under Six Hours

Threat actors are continuing to leverage artificial intelligence AI to streamline their operations, with one financially motivated hacking group employing an autonomous, multi-agent attack framework to carry out a large-scale credential harvesting campaign within six hours. Google Threat...

Defending Retrieval-Augmented Intrusion Detection against Knowledge Poisoning and Prompt Injection

Retrieval-Augmented Generation RAG enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisonin...

CLEAR: Causal Context-Based Agentic Reasoning for Vulnerability Detection

Detecting source code vulnerabilities is increasingly difficult as modern security flaws are rooted in complex causal dependencies between execution flows, control conditions, and program states. Despite recent advances in Large Language Models LLMs and multi-agent frameworks, existing approaches...

Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning

Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct high-level malicious behaviors under partial observability from sparse, dispersed evidence intertwined with benign...

A Knowledge-Based Multi-Agent Framework for Security Control Recommendation

Hardening IT on-premises environments can be a daunting task for teams without access to adequate cybersecurity expertise. In this regard, Decision Support Systems DSS with embedded expert knowledge can assist users by guiding them with security recommendations to meet their objectives. This work...

AutoJack Attack Lets One Web Page Hijack AI Agent for Host Code Execution

Microsoft researchers have detailed an exploit chain, named AutoJack, that turns an AI browsing agent into a delivery vehicle for remote code execution. Steer the agent to load an attacker's web page, and that page's JavaScript can reach a privileged local service on the same machine and spawn a...

Improper Authentication

Overview PraisonAI is a PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent...

LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments

The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. Existing...

Automation-Exploit-Legacy

Automation-Exploit Legacy Prototype This repository contain...

MARD: A Multi-Agent Framework for Robust Android Malware Detection

With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models LLMs...

A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection

Open-source libraries are widely used in modern software development, introducing significant security vulnerabilities. While static analysis tools can identify potential vulnerabilities at scale, they often generate overwhelming reports with high false positive rates. Automated Exploit Generatio...

AEGIS: From Clues to Verdicts -- Graph-Guided Deep Vulnerability Reasoning Via Dialectics and Meta-Auditing

Large Language Models LLMs are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major mitigation paradigms agent-based debate and retrieval augmentation: reasoning in an ungrounded deliberative space that...