Criminal IP is a cyber threat intelligence search engine that detects vulnerabilities in personal and corporate cyber assets in real time and allows users to take preemptive actions. Coming from the idea that individuals and businesses would be able to boost their cyber security by obtaining information about accessing IP addresses in advance, Criminal IP's extensive data of over 4.2 billion IP addresses and counting to provide threat-relevant information about malicious IP addresses, malicious links, phishing websites, certificates, industrial control systems, IoTs, servers, CCTVs, etc.
Using Criminal IP’s four key features (Asset Search, Domain Search, Exploit Search, and Image Search), you can search for IP risk scores and vulnerabilities related to searched IP addresses and domains, vulnerabilities for each service, and assets that are open to cyber attacks in image forms, in respective order.
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Criminal IP's Attack Surface Management (ASM) is an intelligence-driven platform designed to continuously identify, catalog, and oversee all internet-connected assets linked to an organization, including overlooked and shadow resources, enabling teams to understand their actual external exposure from the perspective of potential attackers. This solution integrates automated asset detection with open-source intelligence (OSINT) methods, artificial intelligence enhancements, and sophisticated threat intelligence to reveal exposed hosts, domains, cloud services, IoT devices, and other internet-facing entry points, while also collecting evidence such as screenshots and metadata, and linking findings to known vulnerabilities and attacker techniques. By evaluating exposures through the lens of business relevance and risk, ASM emphasizes vulnerable elements and misconfigurations, providing instantaneous alerts and interactive dashboards that facilitate quicker investigations and remediation efforts. Furthermore, this comprehensive tool empowers organizations to proactively manage their security posture, ensuring that they remain vigilant against emerging threats.
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Resurface
Resurface is a runtime API security tool. Resurface continuous API scanning allows you to detect and respond in real time to API threats and risks. Resurface is a purpose-built tool for API data. It captures all request and response payloads, including GraphQL, to instantly see potential threats and failures. Receive alerts about data breaches for zero-day detection.
Resurface is mapped to OWASP Top10 and alerts on threats with complete security patterns. Resurface is self-hosted and all data is first-party.
Resurface is the only API security system that can be used to perform deep inspections at scale. Resurface detects active attacks and alerts them by processing millions of API calls. Machine learning models detect anomalies and identify low-and slow attack patterns.
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Fork
Fork is a SaaS platform designed for threat modeling that enables both security and product teams to conduct ongoing, risk-oriented assessments of applications by utilizing the established PASTA (Process for Attack Simulation and Threat Analysis) framework. This allows teams to swiftly identify the most probable and significant risks in less than two hours while ensuring that security measures are aligned with business objectives. By merging specialized threat libraries with current vulnerability information and threat intelligence, Fork accurately quantifies residual risks and aids in conducting business impact analyses. The platform also implements quality controls throughout the threat modeling process to enhance overall effectiveness. Additionally, Fork features a consolidated security insights dashboard that links threats directly to the attack surface of your application, while incorporating recognized frameworks and taxonomies like MITRE, OWASP, CWE, CVE (with EPSS), CAPEC, ATT&CK, D3FEND, and ASVS, which facilitates focused mitigation strategies and practical outcomes. This comprehensive approach not only enhances security posture but also fosters collaboration between technical and business teams.
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