Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning

ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicious requests by matching them against the Core Rule Set, identifying well-known attack patterns. Each rule in the CRS is manually assigned a weight, based on the severity of the corresponding attack, and a request is detected as malicious if the sum of the weights of the firing rules exceeds a given threshold. In this work, we show that this simple strategy is largely ineffective for detecting SQL injection (SQLi) attacks, as it tends to block many legitimate requests, while also being vulnerable to adversarial SQLi attacks, i.e., attacks intentionally manipulated to evade detection. To overcome these issues, we design a robust machine learning model, named AdvModSec, which uses the CRS rules as input features, and it is trained to detect adversarial SQLi attacks. Our experiments show that AdvModSec, being trained on the traffic directed towards the protected web services, achieves a better trade-off between detection and false positive rates, improving the detection rate of the vanilla version of ModSecurity with CRS by 21%. Moreover, our approach is able to improve its adversarial robustness against adversarial SQLi attacks by 42%, thereby taking a step forward towards building more robust and trustworthy WAFs

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An Optimized Intruder Model for SAT-based Model-Checking of Security Protocols Abstract

In previous work we showed that automatic SAT-based model-checking techniques based on a reduction of protocol (in)security problems to a sequence of propositional satisfiability problems can be used to effectively find attacks on protocols. In this paper we present an optimized intruder model that may lead in many cases to shorter attacks which can be detected in our framework by generating smaller propositional formulae. The key idea is to assume that some of the abilities of the intruder have instantaneous effect, whereas in the previously adopted approach all the abilities of the intruder were modeled as state transitions. This required non trivial extensions to the SAT-reduction techniques which are formally described in the paper. Experimental results indicate the advantages of the proposed optimization.

SATMC: a SAT-based model checker for security protocols, business processes, and security APIs

We present SATMC 3.0, a SAT-based bounded model checker for security-critical systems that stems from a successful combination of encoding techniques originally developed for planning with techniques developed for the analysis of reactive systems. SATMC has been successfully applied in a variety of application domains (security protocols, security-sensitive business processes, and cryptographic APIs) and for different purposes (design-time security analysis and security testing). SATMC strikes a balance between general purpose model checkers and security protocol analyzers as witnessed by a number of important success stories including the discovery of a serious man-in-the-middle attack on the SAML-based single sign-on (SSO) for Google Apps, an authentication flaw in the SAML 2.0 Web Browser SSO Profile, and a number of attacks on PKCS#11 Security Tokens. SATMC is integrated and used as back-end in a number of research prototypes (e.g., the AVISPA Tool, Tookan, the SPaCIoS Tool) and industrial-strength tools (e.g., the Security Validator plugin for SAP NetWeaver BPM)

Assisting the Deployment of Security-Sensitive Workflows by Finding Execution Scenarios

Part 2: Access Control and AuthorizationInternational audienceTo support the re-use of business process models, an emerging trend in Business Process Management, it is crucial to assist customers during deployment. We study how to do this for an important class of business processes, called security-sensitive workflows, in which execution constraints on the tasks are complemented with authorization constraints (e.g., Separation of Duty) and authorization policies (constraining which users can execute which tasks). We identify the capability of solving Scenario Finding Problems (SFPs), i.e. finding concrete execution scenarios, as crucial in supporting the re-use of security-sensitive workflows. Solutions of SFPs provide evidence that the business process model can be successfully executed under the policy adopted by the customer. We present a technique for solving two SFPs and validate it on real-world business process models taken from an on-line library

Attack Patterns for Black-Box Security Testing of Multi-Party Web Applications

The advent of Software-as-a-Service (SaaS) has led to the development of multi-party web applications (MPWAs). MPWAs rely on core trusted third-party systems (e.g., payment servers, identity providers) and protocols such as Cashier-as-aService (CaaS), Single Sign-On (SSO) to deliver business services to users. Motivated by the large number of attacks discovered against MPWAs and by the lack of a single general-purpose application-agnostic technique to support their discovery, we propose an automatic technique based on attack patterns for black-box, security testing of MPWAs. Our approach stems from the observation that attacks against popular MPWAs share a number of similarities, even if the underlying protocols and services are different. In this paper, we target six different replay attacks, a login CSRF attack and a persistent XSS attack. Firstly, we propose a methodology in which security experts can create attack patterns from known attacks. Secondly, we present a security testing framework that leverages attack patterns to automatically generate test cases for testing the security of MPWAs. We implemented our ideas on top of OWASP ZAP (a popular, open-source penetration testing tool), created seven attack patterns that correspond to thirteen prominent attacks from the literature and discovered twenty one previously unknown vulnerabilities in prominent MPWAs (e.g., twitter.com, developer.linkedin.com, pinterest.com), including MPWAs that do not belong to SSO and CaaS families