In theory, modern web frameworks provide secure ways of accessing databases, making SQL injections a non-issue. The reality looks much different. Among other injection vulnerabilities, SQL injections are still atop the OWASP Top 10, and organizations still frequently fall victim. Therefore there is no way around software security testing solutions that can reliably detect SQL injections.
TL;DR Fuzzing JavaScript is easy now In this post, we introduce you to our new open-source fuzzer for the JavaScript ecosystem, Jazzer.js. Jazzer.js is a coverage-guided, in-process fuzzer for the Node.js platform. It’s based on the experience we gathered developing its namesake Jazzer, our fuzzer for the JVM platform. Internally, Jazzer.js uses libFuzzer as a solid industry-standard engine and brings many of its instrumentation-powered mutation features to JavaScript.
Golang was the first programming language to support fuzzing as a first-class experience in version 1.18. This made it really easy for developers to write fuzz tests. Golang 1.14 introduced native compiler instrumentation for libFuzzer, which enables the use of libFuzzer to fuzz Go code. libFuzzer is one of the most advanced and widely used fuzzing engines and provides the most effective method for Golang Fuzzing.
We are thrilled to announce that we secured our Series A funding round of $12 Million to fulfill our vision of a world where security is a given, not a hope. The round was led by US-based Tola Capital and introduced experienced business angels such as Thomas Dohmke. We will use the funds to add support for more programming languages, provide further dev tool integrations and grow the team.
Coverage-guided fuzzers, like Jazzer, maximize the amount of executed code during fuzzing. This has proven to produce interesting findings deep inside the codebase. Only checking validation rules on the first application layer isn’t providing great benefits, whereas verifying logic in and interactions of deeply embedded components is. To extend the amount of covered code, the fuzzer tries to mutate its input in such a way that it passes existing checks and reaches yet unknown code paths.
Jazzer, our fuzzer for the JVM, is already being used with several JVM languages like Java and Kotlin. Recently, a member of the community asked us whether Jazzer can also fuzz Clojure code. The answer is yes, but it wasn't obvious how to set things up. So we've built a small helper library, jazzer-clj, which contains everything you need to get started with Jazzer for Clojure. There's also an example project to demonstrate the setup.
Application Programming Interface (APIs), allow services to communicate with each other. Naturally, applications that are interconnected through many APIs, require thorough security testing, as each connection could potentially include software vulnerabilities. Since there are different methods to test these junctions, I want to briefly discuss the benefits and weaknesses of the most commonly used API testing methods in this article.
To help contain the damages that arise from the log4j vulnerability, Code Intelligence collaborated with Google’s Open Source Security Team. Together, we implemented effective bug detectors for Remote Code Execution Vulnerabilities (RCEs) to Google’s open source fuzzing framework, OSS-Fuzz.
On December 9th, 2021, the Remote Code Execution (RCE) CVE-2021-44228 in Apache log4j 2 was published and started seeing active exploitation soon after. Since then, development teams have been working hard and tirelessly, trying to fix the issue to prevent (further) damage.
CI Fuzz is a platform for automated security testing that aims to enable developers to ship secure software fast. The platform empowers development teams to automatically deploy continuous REST API security tests with each pull request. Since it enables the instrumentation of entire web service environments, CI Fuzz can create test inputs that are guided by code coverage. This enables it to uncover complex vulnerabilities and edge cases that other tools often overlook.
