Best AI tools for< Vulnerability Researcher >

The website offers an AI-powered binary vulnerability scanner that allows users to upload a binary file for analysis. The tool decompiles the executable, removes filler, cleans, formats, and checks for historical vulnerabilities. It generates function-wise embeddings using a finetuned CodeT5+ Embedding model and checks for similarities against the DiverseVul Dataset. The tool also utilizes SemGrep to check for vulnerabilities in the binary file.

OpenBuckets is a web application designed to help users find and secure open buckets in cloud storage systems. It provides a user-friendly interface for scanning and identifying publicly accessible buckets, allowing users to take necessary actions to secure their data. With OpenBuckets, users can easily detect potential security risks and protect their sensitive information stored in cloud storage. The application is a valuable tool for individuals and organizations looking to enhance their data security measures in the cloud.

Huntr is the world's first bug bounty platform for AI/ML. It provides a single place for security researchers to submit vulnerabilities, ensuring the security and stability of AI/ML applications, including those powered by Open Source Software (OSS).

Cyble is a leading threat intelligence platform offering products and services recognized by top industry analysts. It provides AI-driven cyber threat intelligence solutions for enterprises, governments, and individuals. Cyble's offerings include attack surface management, brand intelligence, dark web monitoring, vulnerability management, takedown and disruption services, third-party risk management, incident management, and more. The platform leverages cutting-edge AI technology to enhance cybersecurity efforts and stay ahead of cyber adversaries.

CensysGPT Beta is a tool that simplifies building queries and empowers users to conduct efficient and effective reconnaissance operations. It enables users to quickly and easily gain insights into hosts on the internet, streamlining the process and allowing for more proactive threat hunting and exposure management.

Protect AI is a comprehensive platform designed to secure AI systems by providing visibility and manageability to detect and mitigate unique AI security threats. The platform empowers organizations to embrace a security-first approach to AI, offering solutions for AI Security Posture Management, ML model security enforcement, AI/ML supply chain vulnerability database, LLM security monitoring, and observability. Protect AI aims to safeguard AI applications and ML systems from potential vulnerabilities, enabling users to build, adopt, and deploy AI models confidently and at scale.

Giskard is an AI testing platform designed to secure Language Model (LLM) agents by continuously testing applications to prevent hallucinations and security issues. It is powered by leading AI researchers and trusted by Enterprise AI teams. Giskard offers features such as continuous testing, exhaustive risk detection, easy testing deployment, cross-team collaboration, and independent validation. The platform enables users to turn business knowledge into AI tests, generate comprehensive test scenarios, and stay protected with continuous Red Teaming that adapts to new threats.

ODIN is a powerful internet scanning search engine designed for scanning and cataloging internet assets. It offers enhanced scanning capabilities, faster refresh rates, and comprehensive visibility into open ports. With over 45 modules covering various aspects like HTTP, Elasticsearch, and Redis, ODIN enriches data and provides accurate and up-to-date information. The application uses AI/ML algorithms to detect exposed buckets, files, and potential vulnerabilities. Users can perform granular searches, access exploit information, and integrate effortlessly with ODIN's API, SDKs, and CLI. ODIN allows users to search for hosts, exposed buckets, exposed files, and subdomains, providing detailed insights and supporting diverse threat intelligence applications.

Glog is an AI application focused on making software more secure by providing remediation advice for security vulnerabilities in software code based on context. It is capable of automatically fixing vulnerabilities, thus reducing security risks and protecting against cyber attacks. The platform utilizes machine learning and AI to enhance software security and agility, ensuring system reliability, integrity, and safety.

Horizon3.ai Solutions offers the NodeZero™ Platform, an AI-powered autonomous penetration testing tool designed to enhance cybersecurity measures. The platform combines expert human analysis by Offensive Security Certified Professionals with automated testing capabilities to streamline compliance processes and proactively identify vulnerabilities. NodeZero empowers organizations to continuously assess their security posture, prioritize fixes, and verify the effectiveness of remediation efforts. With features like internal and external pentesting, rapid response capabilities, AD password audits, phishing impact testing, and attack research, NodeZero is a comprehensive solution for large organizations, ITOps, SecOps, security teams, pentesters, and MSSPs. The platform provides real-time reporting, integrates with existing security tools, reduces operational costs, and helps organizations make data-driven security decisions.

AquilaX is an AI-powered DevSecOps platform that simplifies security and accelerates development processes. It offers a comprehensive suite of security scanning tools, including secret identification, PII scanning, SAST, container scanning, and more. AquilaX is designed to integrate seamlessly into the development workflow, providing fast and accurate results by leveraging AI models trained on extensive datasets. The platform prioritizes developer experience by eliminating noise and false positives, making it a go-to choice for modern Secure-SDLC teams worldwide.

Kindo is an AI-powered platform designed for DevSecOps teams to automate tasks, write doctrine, and orchestrate infrastructure responses. It offers AI-powered Runbook automations to streamline workflows, automate tedious tasks, and enhance security controls. Kindo enables users to offload time-consuming tasks to AI Agents, prioritize critical tasks, and monitor AI-related activities for compliance and informed decision-making. The platform provides a comprehensive vantage point for modern infrastructure defense and instrumentation, allowing users to create repeatable processes, automate vulnerability assessment and remediation, and secure multi-cloud IAM configurations.

Snyk is a developer security platform powered by DeepCode AI, offering solutions for application security, software supply chain security, and secure AI-generated code. It provides comprehensive vulnerability data, license compliance management, and self-service security education. Snyk integrates AI models trained on security-specific data to secure applications and manage tech debt effectively. The platform ensures developer-first security with one-click security fixes and AI-powered recommendations, enhancing productivity while maintaining security standards.

Qwiet AI is a code vulnerability detection platform that accelerates secure coding by uncovering, prioritizing, and generating fixes for top vulnerabilities with a single scan. It offers features such as AI-enhanced SAST, contextual SCA, AI AutoFix, Container Security, SBOM, and Secrets detection. Qwiet AI helps InfoSec teams in companies to accurately pinpoint and autofix risks in their code, reducing false positives and remediation time. The platform provides a unified vulnerability dashboard, prioritizes risks, and offers tailored fix suggestions based on the full context of the code.

CloudDefense.AI is an industry-leading multi-layered Cloud Native Application Protection Platform (CNAPP) that safeguards cloud infrastructure and cloud-native apps with expertise, precision, and confidence. It offers comprehensive cloud security solutions, vulnerability management, compliance, and application security testing. The platform utilizes advanced AI technology to proactively detect and analyze real-time threats, ensuring robust protection for businesses against cyber threats.

SANS AI Cybersecurity Hackathon is a global virtual competition that challenges participants to design and build AI-driven solutions to secure systems, protect data, and counter emerging cyber threats. The hackathon offers a platform for cybersecurity professionals and students to showcase their creativity and technical expertise, connect with a global community, and make a real-world impact through AI innovation. Participants are required to create open-source solutions addressing pressing cybersecurity challenges by integrating AI, with a focus on areas like threat detection, incident response, vulnerability scanning, security dashboards, digital forensics, and more.

BigBear.ai is an AI-powered decision intelligence solutions provider that offers services across various industries including Government & Defense, Manufacturing & Warehouse Operations, Healthcare & Life Sciences. They specialize in optimizing operational efficiency, force deployment, supply chain management, autonomous systems management, and vulnerability detection. Their solutions are designed to improve situational awareness, streamline production processes, and enhance patient care delivery settings.

DepsHub is an AI-powered tool designed to simplify dependency management for software development teams. It offers automatic dependency updates, license checks, and security vulnerability scanning to ensure teams stay secure and up-to-date. With features like noise-free dependency management, cross-repository overview, license compliance, and security alerts, DepsHub streamlines the process of managing dependencies for teams of any size. The AI-powered engine analyzes library changelogs, release notes, and codebases to automatically update dependencies, including handling breaking changes. DepsHub supports a wide range of languages and frameworks, making it easy for teams to integrate and get started in minutes. By saving time and effort on dependency management, DepsHub allows developers to focus on writing code that matters, while keeping it secure and up to date.

CyberUpgrade.net is an AI-powered platform that offers comprehensive cybersecurity and compliance solutions for organizations of all sizes. It provides automated compliance, risk management, vendor risk assessment, policy management, audit management, and 24/7 security support. The platform features a cloud vulnerability scanner, security awareness training, pentesting, business continuity planning, disaster recovery planning, and an AI-powered assistant for seamless security support. CyberUpgrade helps CTOs understand their organization's security status, proposes improvement plans, guides execution, and prepares compliance documentation with a push of a button. It engages every employee individually for evidence collection and situation analysis, ensuring real cybersecurity measures are in place.

Equixly is an AI-powered application designed to help users secure their APIs by identifying vulnerabilities and weaknesses through continuous security testing. The platform offers features such as scalable API PenTesting, attack simulation, mapping of attack surfaces, compliance simplification, and data exposure minimization. Equixly aims to streamline the process of identifying and fixing API security risks, ultimately enabling users to release secure code faster and reduce their attack surface.

FiniteMonkey is an advanced vulnerability mining engine powered purely by GPT, requiring no prior knowledge base or fine-tuning. Its effectiveness significantly surpasses most current related research approaches. The tool is task-driven, prompt-driven, and focuses on prompt design, leveraging 'deception' and hallucination as key mechanics. It has helped identify vulnerabilities worth over $60,000 in bounties. The tool requires PostgreSQL database, OpenAI API access, and Python environment for setup. It supports various languages like Solidity, Rust, Python, Move, Cairo, Tact, Func, Java, and Fake Solidity for scanning. FiniteMonkey is best suited for logic vulnerability mining in real projects, not recommended for academic vulnerability testing. GPT-4-turbo is recommended for optimal results with an average scan time of 2-3 hours for medium projects. The tool provides detailed scanning results guide and implementation tips for users.

SinkFinder + LLM is a closed-source semi-automatic vulnerability discovery tool that performs static code analysis on jar/war/zip files. It enhances the capability of LLM large models to verify path reachability and assess the trustworthiness score of the path based on the contextual code environment. Users can customize class and jar exclusions, depth of recursive search, and other parameters through command-line arguments. The tool generates rule.json configuration file after each run and requires configuration of the DASHSCOPE_API_KEY for LLM capabilities. The tool provides detailed logs on high-risk paths, LLM results, and other findings. Rules.json file contains sink rules for various vulnerability types with severity levels and corresponding sink methods.

Agentic Security is an open-source vulnerability scanner designed for safety scanning, offering customizable rule sets and agent-based attacks. It provides comprehensive fuzzing for any LLMs, LLM API integration, and stress testing with a wide range of fuzzing and attack techniques. The tool is not a foolproof solution but aims to enhance security measures against potential threats. It offers installation via pip and supports quick start commands for easy setup. Users can utilize the tool for LLM integration, adding custom datasets, running CI checks, extending dataset collections, and dynamic datasets with mutations. The tool also includes a probe endpoint for integration testing. The roadmap includes expanding dataset variety, introducing new attack vectors, developing an attacker LLM, and integrating OWASP Top 10 classification.

Academic_LLM_Sec_Papers is a curated collection of academic papers related to LLM Security Application. The repository includes papers sorted by conference name and published year, covering topics such as large language models for blockchain security, software engineering, machine learning, and more. Developers and researchers are welcome to contribute additional published papers to the list. The repository also provides information on listed conferences and journals related to security, networking, software engineering, and cryptography. The papers cover a wide range of topics including privacy risks, ethical concerns, vulnerabilities, threat modeling, code analysis, fuzzing, and more.

CodeLLM Paper repository provides a curated list of research papers focused on Large Language Models (LLMs) for code. It aims to facilitate researchers and practitioners in exploring the rapidly growing body of literature on this topic. The papers are systematically collected from various top-tier venues, categorized, and labeled for easier navigation. The selection strategy involves abstract extraction, keyword matching, relevance check using LLMs, and manual labeling. The papers are categorized based on Application, Principle, and Research Paradigm dimensions. Contributions to expand the repository are welcome through PR submission, issue submission, or request for batch updates. The repository is intended solely for research purposes, with raw data sourced from publicly available information on ACM, IEEE, and corresponding conference websites.

HackBot is an AI-powered cybersecurity chatbot designed to provide accurate answers to cybersecurity-related queries, conduct code analysis, and scan analysis. It utilizes the Meta-LLama2 AI model through the 'LlamaCpp' library to respond coherently. The chatbot offers features like local AI/Runpod deployment support, cybersecurity chat assistance, interactive interface, clear output presentation, static code analysis, and vulnerability analysis. Users can interact with HackBot through a command-line interface and utilize it for various cybersecurity tasks.

Awesome LLM Security is a curated collection of tools, documents, and projects related to Large Language Model (LLM) security. It covers various aspects of LLM security including white-box, black-box, and backdoor attacks, defense mechanisms, platform security, and surveys. The repository provides resources for researchers and practitioners interested in understanding and safeguarding LLMs against adversarial attacks. It also includes a list of tools specifically designed for testing and enhancing LLM security.

This repository is a comprehensive survey of harmful fine-tuning attacks and defenses for large language models (LLMs). It provides a curated list of must-read papers on the topic, covering various aspects such as alignment stage defenses, fine-tuning stage defenses, post-fine-tuning stage defenses, mechanical studies, benchmarks, and attacks/defenses for federated fine-tuning. The repository aims to keep researchers updated on the latest developments in the field and offers insights into the vulnerabilities and safeguards related to fine-tuning LLMs.

The Agentic Radar is a security scanner designed to analyze and assess agentic systems for security and operational insights. It helps users understand how agentic systems function, identify potential vulnerabilities, and create security reports. The tool includes workflow visualization, tool identification, and vulnerability mapping, providing a comprehensive HTML report for easy reviewing and sharing. It simplifies the process of assessing complex workflows and multiple tools used in agentic systems, offering a structured view of potential risks and security frameworks.

Awesome LLM for APR is a repository dedicated to exploring the capabilities of Large Language Models (LLMs) in Automated Program Repair (APR). It provides a comprehensive collection of research papers, tools, and resources related to using LLMs for various scenarios such as repairing semantic bugs, security vulnerabilities, syntax errors, programming problems, static warnings, self-debugging, type errors, web UI tests, smart contracts, hardware bugs, performance bugs, API misuses, crash bugs, test case repairs, formal proofs, GitHub issues, code reviews, motion planners, human studies, and patch correctness assessments. The repository serves as a valuable reference for researchers and practitioners interested in leveraging LLMs for automated program repair.

IPEX-LLM is a PyTorch library for running Large Language Models (LLMs) on Intel CPUs and GPUs with very low latency. It provides seamless integration with various LLM frameworks and tools, including llama.cpp, ollama, Text-Generation-WebUI, HuggingFace transformers, and more. IPEX-LLM has been optimized and verified on over 50 LLM models, including LLaMA, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, and RWKV. It supports a range of low-bit inference formats, including INT4, FP8, FP4, INT8, INT2, FP16, and BF16, as well as finetuning capabilities for LoRA, QLoRA, DPO, QA-LoRA, and ReLoRA. IPEX-LLM is actively maintained and updated with new features and optimizations, making it a valuable tool for researchers, developers, and anyone interested in exploring and utilizing LLMs.

This repository contains materials for the paper 'How Far Have We Gone in Vulnerability Detection Using Large Language Model'. It provides a tool for evaluating vulnerability detection models using datasets such as d2a, ctf, magma, big-vul, and devign. Users can query the model 'Llama-2-7b-chat-hf' and store results in a SQLite database for analysis. The tool supports binary and multiple classification tasks with concurrency settings. Additionally, users can evaluate the results and generate a CSV file with metrics for each dataset and prompt type.

Garak is a vulnerability scanner designed for LLMs (Large Language Models) that checks for various weaknesses such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. It combines static, dynamic, and adaptive probes to explore vulnerabilities in LLMs. Garak is a free tool developed for red-teaming and assessment purposes, focusing on making LLMs or dialog systems fail. It supports various LLM models and can be used to assess their security and robustness.

FigStep is a black-box jailbreaking algorithm against large vision-language models (VLMs). It feeds harmful instructions through the image channel and uses benign text prompts to induce VLMs to output contents that violate common AI safety policies. The tool highlights the vulnerability of VLMs to jailbreaking attacks, emphasizing the need for safety alignments between visual and textual modalities.

This is a vulnerability mining engine purely based on GPT, requiring no prior knowledge base, no fine-tuning, yet its effectiveness can overwhelmingly surpass most of the current related research. The core idea revolves around being task-driven, not question-driven, driven by prompts, not by code, and focused on prompt design, not model design. The essence is encapsulated in one word: deception. It is a type of code understanding logic vulnerability mining that fully stimulates the capabilities of GPT, suitable for real actual projects.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.

SploitCraft is a curated collection of security exploits, penetration testing techniques, and vulnerability demonstrations intended to help professionals and enthusiasts understand and demonstrate the latest in cybersecurity threats and offensive techniques. The repository is organized into folders based on specific topics, each containing directories and detailed READMEs with step-by-step instructions. Contributions from the community are welcome, with a focus on adding new proof of concepts or expanding existing ones while adhering to the current structure and format of the repository.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

LF AI & Data Landscape is a map to explore open source projects in the AI & Data domains, highlighting companies that are members of LF AI & Data. It showcases members of the Foundation and is modelled after the Cloud Native Computing Foundation landscape. The landscape includes current version, interactive version, new entries, logos, proper SVGs, corrections, external data, best practices badge, non-updated items, license, formats, installation, vulnerability reporting, and adjusting the landscape view.

Expert in CVEs and cybersecurity vulnerabilities, providing precise information from the National Vulnerability Database.

AppSec & Bug Bounty

Detailed CVE analysis, focusing on chaining and APTs, using a fixed schema.

GPT expert on CISA's KEV Catalog and CVE existence check

Your personal Ethereum magician — Simply ask a question or provide a code sample for insights into vulnerabilities, gas optimizations, and best practices. Don't be shy to ask about tooling and legendary attacks.

Cybersecurity simulation

Updates on CVEs from GitHub repo in table format.

Expert in analyzing vulnerabilities with ransomware focus with intelligence powered by Phoenix Security

Web Vulnerability Academy (WVA) is an interactive tutor designed to introduce users to web vulnerabilities while also providing them with opportunities to assess and enhance their knowledge through testing.

Your ally in navigating the CVE deluge. Expert insights for prioritizing and remediating vulnerabilities.

Ensures software security through comprehensive testing techniques.

A PHP Security Expert offering guidance on secure coding practices.

ChatGPT Especializado em DevSecOps

Assists in writing detailed security reports.

Cyber Asset Identification Expert

Your AI-powered sentinel for code! Scans for vulnerabilities, offers security tips, and educates on best practices in cybersecurity. 🔍🔐

Assistant for pentesting apis

Your personal security tool

Comprehensive threat modeling

Betterscan.io DevSecOps