skyvern

🐉 Automate Browser-based workflows using LLMs and Computer Vision 🐉...

ClawGuard

ClawGuard 🛡️ 中文版 Our Project:https://github.com/SafeAgent-Beihang/clawguard ClawGuard is a security toolkit designed to mitigate risks associated with autonomous agents, such as OpenClaw and other LLM-driven entities. As agents gain more autonomy to execute code, access APIs, and manage files,...

opentaint

El motor de analisis de taint de codigo abierto para la era de la IA Analisis de taint formal para la seguridad de aplicaciones — encuentra lo que los motores de coincidencia de patrones AST pasan por alto, permite a los agentes LLM aplicar vulnerabilidades como reglas, escala donde ninguno puede...

ConcoLLMic

ConcoLLMic: Agentic Concolic Execution Paper : IEEE S&P 2026 ConcoLLMic is the first language- and theory-agnostic concolic executor powered by LLM agents. Unlike traditional symbolic execution tools that require language-specific implementations and struggle with constraint solving, ConcoLLMic:...

anamnesis-release

Anamnesis: LLM Exploit Generation Evaluation This repository contains the evaluation framework for studying how LLM agents generate exploits from vulnerability reports in the presence of exploit mitigations. Given a bug report and proof-of-concept trigger, agents analyze vulnerable software and...

AgentWatcher

AgentWatcher AgentWatcher is a detection-based defense against indirect prompt injection in LLM agents. It first runs causal context attribution over untrusted context to find the most influential contexts, then applies a monitor LLM that classifies those contexts under explicit, customizable...

agentic-dm-gateway

Agentic DM Gateway Security control plane for LLM agents over private chat typically Discord DMs. It sits in front of your agent. It decides who may talk, whether the session is unlocked, whether the process is paused, and whether this message is safe enough to forward. Your model and tools stay...

llm-agent-testbed

🛡️ LLM Agent Security Testbed Empirical Vulnerability & Defense Harness for Tool-Calling LLM Agents A disciplined security testbed testing whether tool-equipped LLM agents can be manipulated into unauthorized data exfiltration via prompt injection, role-claim social engineering, and confused-deput...

Reveree: Diagnosing LLM Reverse-Engineering Agents

Reverse engineering RE is critical to security tasks such as malware analysis and vulnerability discovery, and large language model LLM agents are increasingly able to perform it autonomously. Capture-the-flag CTF RE challenges have become the standard proxy for measuring this capability, but...

Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents

Autonomous Large Language Model LLM agents are increasingly deployed to conduct complex tasks by interacting with external tools, APIs, and memory stores. However, processing untrusted external data exposes these agents to severe security threats, such as indirect prompt injection and unauthorize...

Taint-Style Vulnerability Detection and Confirmation for Node.Js Packages Using LLM Agent Reasoning

The rapidly evolving Node$.$js ecosystem currently includes millions of packages and is a critical part of modern software supply chains, making vulnerability detection of Node$.$js packages increasingly important. However, traditional program analysis struggles in this setting because of dynamic...

TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs

Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing SAST tools have long served as the first line of defense, they suffer from high false-positive rates. This article presents TitanCA, a collaborative proje...

CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environments

The advancement of Large Language Models LLMs has raised concerns regarding their dual-use potential in cybersecurity. Existing evaluation frameworks overwhelmingly focus on Information Technology IT environments, failing to capture the constraints, and specialized protocols of Operational...

Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study

Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments, making leakage risks poorly understood. We present the first large-scale empirical study of this problem, analyzing 17,022 skills sampled from 170,226 on SkillsMP usi...

From Storage to Steering: Memory Control Flow Attacks on LLM Agents

Modern agentic systems allow Large Language Model LLM agents to tackle complex tasks through extensive tool usage, forming structured control flows of tool selection and execution. Existing security analyses often treat these control flows as ephemeral, one-off sessions, overlooking the persisten...

ChainFuzzer: Greybox Fuzzing for Workflow-Level Multi-Tool Vulnerabilities in LLM Agents

Tool-augmented LLM agents increasingly rely on multi-step, multi-tool workflows to complete real tasks. This design expands the attack surface, because data produced by one tool can be persisted and later reused as input to another tool, enabling exploitable source-to-sink dataflows that only...

Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks

LLM agents are evolving rapidly, powered by code execution, tools, and the recently introduced agent skills feature. Skills allow users to extend LLM applications with specialized third-party code, knowledge, and instructions. Although this can extend agent capabilities to new domains, it creates...

Searching for Privacy Risks in LLM Agents Via Simulation

The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extract sensitive information. These dynamic dialogues enable adaptive attack strategies that can cause severe privacy...

AgentVigil: Generic Black-Box Red-Teaming for Indirect Prompt Injection against LLM Agents

The strong planning and reasoning capabilities of Large Language Models LLMs have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk:...

Using LLMs to Exploit Vulnerabilities

Interesting research: "Teams of LLM Agents can Exploit Zero-Day Vulnerabilities." Abstract: LLM agents have become increasingly sophisticated, especially in the realm of cybersecurity. Researchers have shown that LLM agents can exploit real-world vulnerabilities when given a description of the...