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Description

Identify the vulnerabilities within your API landscape in a matter of minutes, uncovering business logic weaknesses and safeguarding your applications from even the most advanced threats. This solution requires no additional agents or modifications to your existing infrastructure. Experience the quickest return on investment while obtaining a detailed assessment of your API security status within just 15 minutes. Backed by extensive API security knowledge created by our dedicated research team, this tool is compatible with all APIs across various environments. Escape presents a distinctive methodology for API security via agentless scans, allowing you to quickly visualize all your exposed APIs alongside their contextual information. Gather essential insights about your APIs such as endpoint URLs, methods, response codes, and relevant metadata to pinpoint possible security vulnerabilities, areas of sensitive data exposure, and potential attack vectors. Ensure comprehensive security coverage with over 104 testing parameters, encompassing OWASP standards, business logic assessments, and access control evaluations. Additionally, effortlessly incorporate Escape into your CI/CD workflows using platforms like Github Actions or Gitlab CI for automated security scanning, enhancing your overall security posture. This innovative tool not only streamlines API security but also empowers teams to act proactively against emerging threats.

Description

open-appsec is an open-source initiative that builds on machine learning to provide pre-emptive web app & API threat protection against OWASP-Top-10 and zero-day attacks. It can be deployed as add-on to Kubernetes Ingress, NGINX, Envoy and API Gateways. The open-appsec engine learns how users normally interact with your web application. It then uses this information to automatically detect requests that fall outside of normal operations, and sends those requests for further analysis to decide whether the request is malicious or not. open-appsec uses two machine learning models: 1. A supervised model that was trained offline based on millions of requests, both malicious and benign. 2. An unsupervised model that is being built in real time in the protected environment. This model uses traffic patterns specific to the environment. open-oppsec simplifies maintenance as there is no threat signature upkeep and exception handling, like common in many WAF solutions.