Nuclei AI citations

Here are some of the best AI penetration testing tools: 1. {{[[Nessus]]:https://www.tenable.com/products/nessus}} : Nessus is widely used for vulnerability scanning and includes AI-driven features to enhance its scanning capabilities. 2. {{[[Burp Suite]]:https://portswigger.net/burp}} : Burp Suite offers advanced features for web application security testing, including AI-assisted scanning and vulnerability detection. 3. {{[[Metasploit]]:https://www.metasploit.com}} : Metasploit is a penetration testing framework that incorporates AI to automate the discovery and exploitation of vulnerabilities. 4. {{[[Cyborg Hawk]]:https://cyborghawk.com}} : Cyborg Hawk utilizes AI to provide automated penetration testing and vulnerability assessments for various environments. 5. {{[[AI-based Security Testing Framework (ASTF)]]:https://www.astf.io}} : ASTF is designed to leverage AI for automated security testing, focusing on web applications and APIs. 6. {{[[Prowler]]:https://prowler.cloud}} : Prowler is an open-source security tool that uses AI to assess AWS security configurations and compliance. 7. {{[[AppScan]]:https://www.ibm.com/security/application-security/appscan}} : IBM's AppScan integrates AI to enhance application security testing and vulnerability management. 8. {{[[Qualys]]:https://www.qualys.com}} : Qualys provides cloud-based security and compliance solutions, utilizing AI for vulnerability management and threat detection. These tools leverage AI to improve efficiency and effectiveness in penetration testing and vulnerability assessments.

The best AI penetration testing tools in 2025 include: - {{[[PentestGPT]]:https://kratikal.com/blog/top-ai-driven-pentest-tools/}}: An AI-powered assistant that automates network scanning, reconnaissance, and reporting, helping both novices and experts streamline penetration testing. - {{[[Garak]]:https://kratikal.com/blog/top-ai-driven-pentest-tools/}}: A specialized AI tool for security testing of large language models (LLMs) and AI systems, offering automated red teaming, jailbreak testing, and context-aware attack simulations. - {{[[Mindgard]]:https://mindgard.ai/blog/top-ai-pentesting-tools}}: An AI-driven offensive security platform focused on identifying and mitigating AI-specific vulnerabilities, simulating real-world attacks on AI systems. - {{[[Burp Suite Professional]]:https://kratikal.com/blog/top-ai-driven-pentest-tools/}}: A classic web pentesting tool enhanced with AI-based plugins that analyze web applications and identify attack vectors automatically. - {{[[Microsoft Counterfit]]:https://www.cybernx.com/ai-pentesting-tools/}}: An open-source AI security tool for adversarial attack simulations on AI models, supporting multiple ML frameworks. - {{[[IBM Adversarial Robustness Toolbox (ART)]]:https://www.cybernx.com/ai-pentesting-tools/}}: An open-source Python library for adversarial machine learning testing, supporting white-box and black-box approaches. - {{[[Metasploit with AI integrations]]:https://www.h-x.technology/blog/review-ai-tools-pentest-2025}}: A well-known penetration testing framework augmented with AI modules to automate exploit selection. - {{[[OpenVAS with AI analytics]]:https://www.h-x.technology/blog/review-ai-tools-pentest-2025}}: A vulnerability scanner using machine learning to prioritize issues based on infrastructure context. - {{[[Hadrian]]:https://escape.tech/blog/top-automated-pentesting-tools/}}: An automated penetration testing platform that continuously probes assets in real-time, mimicking attacker behavior and validating exploitation paths. Additional notable tools include {{[[PyRIT]]:https://mindgard.ai/blog/top-ai-pentesting-tools}} for AI-driven WiFi password cracking and {{[[Cobalt Strike with AI scripts]]:https://www.h-x.technology/blog/review-ai-tools-pentest-2025}} for red team operations enhanced with AI. These tools vary in focus from general cybersecurity pentesting to specialized AI and LLM security testing, providing a broad spectrum of AI-assisted capabilities for penetration testers in 2025[1][2][3][4][5][6][7].

Here are the best AI penetration testing tools: 1. {{[[Metasploit]]:https://www.metasploit.com/}} : A powerful open-source framework for developing and executing exploit code against remote targets. 2. {{[[Kali Linux]]:https://www.kali.org/}} : A Linux distribution specifically designed for advanced penetration testing and security auditing. 3. {{[[Nmap]]:https://nmap.org/}} : An open-source network discovery and security auditing tool with AI-enhanced scanning capabilities. 4. {{[[Burp Suite]]:https://portswigger.net/burp}} : A comprehensive web vulnerability scanner with machine learning-powered features. 5. {{[[Nuclei]]:https://github.com/projectdiscovery/nuclei}} : An AI-driven vulnerability scanner that uses customizable templates for threat detection. 6. {{[[ChatGPT]]:https://chat.openai.com/}} : Can assist in generating penetration testing scripts and providing security analysis insights. 7. {{[[Acunetix]]:https://www.acunetix.com/}} : An automated web vulnerability scanner with AI-powered scanning techniques. 8. {{[[Nessus]]:https://www.tenable.com/products/nessus}} : A comprehensive vulnerability assessment tool with machine learning capabilities. 9. {{[[OWASP ZAP]]:https://www.zaproxy.org/}} : An open-source web application security scanner with automated testing features. 10. {{[[Sqlmap]]:http://sqlmap.org/}} : An AI-enhanced tool for detecting and exploiting SQL injection vulnerabilities.

Here are some of the top AI pathology platforms: 1. {{[[PathAI]]:https://pathai.com}} : PathAI provides advanced machine learning algorithms to assist pathologists in diagnosing diseases more accurately and efficiently. 2. {{[[Proscia]]:https://proscia.com}} : Proscia offers a digital pathology platform that leverages AI to enhance the analysis of pathology images and improve workflow. 3. {{[[Tempus]]:https://tempus.com}} : Tempus integrates AI with clinical and molecular data to support personalized medicine in oncology and pathology. 4. {{[[Deep Lens]]:https://deeplens.com}} : Deep Lens uses AI to streamline the pathology workflow and improve patient outcomes through better diagnostic accuracy. 5. {{[[Paige]]:https://paige.ai}} : Paige utilizes AI technology to assist pathologists in cancer diagnosis by providing tools for image analysis and interpretation. 6. {{[[IBM Watson Health]]:https://www.ibm.com/watson-health}} : IBM Watson Health employs AI to analyze pathology data and support clinical decision-making in healthcare. 7. {{[[Qure.ai]]:https://qure.ai}} : Qure.ai focuses on using AI for radiology and pathology, providing tools to automate image interpretation and enhance diagnostic accuracy. 8. {{[[Aiforia]]:https://www.aiforia.com}} : Aiforia offers AI-powered image analysis tools for pathology, enabling faster and more accurate diagnostics. These platforms are at the forefront of integrating AI into pathology to improve diagnostic processes and patient care.

## Leading AI Pathology Platforms The digital pathology landscape is rapidly evolving, with several platforms emerging as leaders in AI-powered diagnostics, workflow optimization, and research collaboration. Here are the top platforms, their key features, and their industry positioning: {{[[Ibex Medical Analytics]]:https://lumeadigital.com/top-10-ai-companies-for-digital-pathology/}} : Currently the most widely deployed AI platform in pathology, developed by pathologists for pathologists, with a focus on improving diagnostic accuracy, streamlining workflows, and enabling better clinical outcomes for leading healthcare organizations worldwide[1]. {{[[Indica Labs]]:https://lumeadigital.com/top-10-ai-companies-for-digital-pathology/}} : Known for its HALO and HALO AI platforms, which streamline image analysis and workflow management in digital pathology, serving both diagnostic labs and pharmaceutical research teams[1][4]. {{[[Mindpeak]]:https://lumeadigital.com/top-10-ai-companies-for-digital-pathology/}} : A German company focused on automating repetitive diagnostic tasks to help pathologists concentrate on complex cases, with demonstrated success in improving HER2 scoring accuracy for breast cancer diagnostics[1][2]. {{[[Owkin]]:https://lumeadigital.com/top-10-ai-companies-for-digital-pathology/}} : Specializes in using AI for drug discovery, clinical trial optimization, and building diagnostic tools that enhance patient outcomes, founded by experts in clinical research and machine learning[1]. {{[[PathAI]]:https://www.pathai.com}} : A major innovator with the cloud-native AISight platform, which centralizes case and image management while integrating advanced AI tools for histopathology. PathAI’s network includes hundreds of pathologists and millions of annotations, supporting both clinical diagnostics and biopharma research. The AISight Dx platform has FDA clearance for primary diagnosis and is used by top biopharma companies and leading cancer centers[3][5][9]. {{[[Proscia]]:https://proscia.com/digital-pathology-and-ai-highlights-from-asco-2025/}} : Offers the Concentriq platform, which combines enterprise-scale slide management, annotation, and integrated AI applications for biomarker scoring and tissue detection, supporting both clinical and research workflows[4]. {{[[Aiforia]]:https://imerit.net/resources/blog/top-annotation-services-and-tooling-providers-for-digital-pathology-ai-in-2025/}} : Provides a cloud-based platform for building custom AI models and annotation pipelines, with strong collaboration tools for distributed teams, though it requires in-house pathology expertise for annotation[4]. {{[[Gestalt Diagnostics]]:https://imerit.net/resources/blog/top-annotation-services-and-tooling-providers-for-digital-pathology-ai-in-2025/}} : Delivers the PathFlow platform, emphasizing lab-ready digital pathology annotation workflows and interoperability across multi-vendor whole-slide imaging scanners[4]. {{[[Nuclei.io]]:https://med.stanford.edu/news/all-news/2025/09/ai-tool-pathology.html}} : Developed at Stanford Medicine, this AI framework adapts to individual pathologist workflows, offering personalized assistance in cell identification and enabling collaborative model-sharing among clinicians[6]. {{[[aetherAI]]:https://www.pathologyinformatics.org/meet-our-pi-summit-2025-sponsors}} : Empowers pathologists with whole-slide imaging, AI-assisted diagnosis, and cloud-based pathology platforms, though detailed public information on specific products is less widely available[8]. ## Platform Comparison Table | Platform | Key Strengths | Clinical/Research Focus | Notable Features | |---------------------------|----------------------------------------------------|-----------------------------------|------------------------------------------| | Ibex Medical Analytics | Widest deployment, pathologist-developed | Clinical diagnostics | Streamlined workflows, improved outcomes | | Indica Labs (HALO AI) | Image analysis, workflow management | Diagnostics & pharma research | HALO AI, bioinformatics services | | Mindpeak | Automation of routine tasks | Clinical diagnostics | HER2 scoring, diagnostic agreement | | Owkin | Drug discovery, clinical trials | Research & diagnostics | Predictive biomarkers, trial optimization| | PathAI (AISight) | Cloud-native, large pathologist network | Clinical & biopharma | FDA-cleared, Precision Pathology Network | | Proscia (Concentriq) | Enterprise slide management, AI integration | Clinical & research | Biomarker scoring, tissue detection | | Aiforia | Custom AI/annotation pipelines | Research | Collaboration tools, model training | | Gestalt Diagnostics | Lab-ready annotation, interoperability | Clinical | DICOM-compliant, audit trails | | Nuclei.io (Stanford) | Personalized AI assistance, collaboration | Clinical | Human-in-the-loop, model sharing | | aetherAI | Whole-slide imaging, cloud platforms | Clinical | AI-assisted diagnosis | ## Industry Trends The AI pathology market is experiencing rapid growth, with a shift toward integrated, end-to-end workflow platforms that combine image management, AI analysis, and collaboration tools[7]. Leading platforms are increasingly focused on regulatory compliance, clinical validation, and seamless integration into existing laboratory systems. These platforms are not only improving diagnostic accuracy and efficiency but are also enabling new approaches to personalized medicine, biomarker discovery, and drug development. Collaboration between AI developers, pathologists, and biopharma is driving the next wave of innovation in pathology.

Top AI Pathology Platforms: 1. {{[[Paige]]:https://www.paige.ai}} : Leading AI platform for cancer diagnostics, using deep learning for pathology image analysis. 2. {{[[Proscia]]:https://www.proscia.com}} : Digital pathology software with AI-powered diagnostic tools and workflow optimization. 3. {{[[Ibex]]:https://www.ibexmedical.com}} : AI-driven computational pathology platform for cancer detection and analysis. 4. {{[[PathAI]]:https://www.pathai.com}} : Advanced machine learning platform for precision pathology and research. 5. {{[[Philips Digital Pathology]]:https://www.philips.com/healthcare/product/HCNOCTN50/digital-pathology-solutions}} : Comprehensive digital pathology solution with AI-assisted diagnostics. 6. {{[[Visiopharm]]:https://visiopharm.com}} : AI and image analysis platform for precision oncology and digital pathology. 7. {{[[Aiforia]]:https://aiforia.com}} : Cloud-based AI platform for medical image analysis and pathology research. These platforms leverage artificial intelligence to enhance diagnostic accuracy, streamline workflows, and support pathologists in medical image interpretation.

{{[[PathAI]]:https://pathai.com}} : PathAI develops AI-powered technology to improve diagnostic accuracy and accelerate the development of new therapeutics by providing quantitative insights from pathology images for research and clinical use, particularly in oncology and NASH. {{[[Paige.AI]]: