A message authentication code (MAC) or a digital signature may be used to authenticate a message and to protect its integrity. When checking a signature, it is better to use constant-time algorithm. Otherwise, an attacker may be able to forge a valid signature for an arbitrary message by running a timing attack. Although it is a pretty sophisticated attack, sometimes it can be a real threat. Let’s see how such issues may be detected with CodeQL in Java applications.
If you use Jackson Databind library and run a security scanner, you might have received quite a lot of alerts about deserialization vulnerabilities. In the past, a new CVE pop up nearly every month when someone discovered a new deserialization gadget that could be used to exploit an application. Fortunately, the project doesn’t assign CVEs for new deserialization gadgets anymore. It makes sense because an application that uses Jackson libraries is not vulnerable by default. However, if the application uses Jackson libraries in a certain way, it may be in danger.
Below I’ll show how CodeQL can be used to to check whether or not an application is vulnerable to deserialization attacks.
Continue readingJava RMI uses the default Java deserialization mechanism for passing parameters during remote method invocations. In other words, RMI uses ObjectInputStream that is a well-known unsafe deserialization mechanism. If an attacker can find and send a deserialization gadget to a vulnerable remote method, in the worst case it can result in arbitrary code execution.
I recently wrote a CodeQL query that looks for dangerous remote objects registered in an RMI registry. This post describes the vulnerability and how the query works.
Continue readingRecently I wrote a post about detecting JEXL injections with CodeQL. JEXL is a library that provides an interpreter for a simple expression language (EL). This time, I’ll talk about injections with Jakarta Expression Language, and how they can be found with CodeQL.
Recently WhiteSource security scanner started reporting WS-2016-7107 against Spring-based applications. This is an old issue in Spring Security that was reported in 2016. Unfortunately, at the moment of writing it, the issue has not been fixed yet. But there is a pull request that should address it. The problem is that CSRF tokens generated by Spring Security are vulnerable to the BREACH attack. The attack is even older — it was published in 2013. The BREACH attack is similar to the CRIME attack but BREACH doesn’t need TLS compression.
(you can also read it on Medium)
In this blog post, I’ll talk about detecting unsafe Spring Exporters with a CodeQL query. First, I’ll describe the issue that received CVE-2016-1000027. Next, I’ll show what a vulnerable code looks like and how the issue can be mitigated in an application. Then, I’ll describe how the CodeQL query works. In addition, I’ll show a couple of vulnerabilities that have been found by the query.
(you can also read it on Medium)
I recently wrote a blog post about a project I have been working on at SAP for a year. The project is called Fosstars. It is an open-source Java-based framework for defining ratings that help to assess security, activity and other properties of open source projects. Currently, Fosstars offers a comprehensive security rating that helps to identify open source components that can be a security risk for an application. The blog post describes how Fosstars work:
https://blogs.sap.com/2021/02/09/fosstars-a-framework-for-defining-ratings-for-open-source-projects/
I am planning to write a few more articles. One is going to talk about the security rating in detail, and another one is going to be about calculating security ratings with Fosstars and GitHub actions.
In this post, I’ll talk about a CodeQL query for detecting JEXL Expression Language injection vulnerabilities. First, I’ll give a brief overview of expression languages in general and JEXL in particular. Next, I’ll explain what Expression Language injection is and how to prevent it. Then, I’ll describe how the CodeQL query works. In addition, I’ll show a couple of vulnerabilities that have been found by the query.
(you can also read it on Medium)
A photoresistor or a light-dependent resistor (LDR) is a resistor that changes its value (resistance) depending on light intensity. More precisely, when light falls upon it, the resistance decreases. It is normally used as a light or dark detector. For example, it may be used in a circuit that turns lights in a room on when it gets dark. Let’s see how we use a photoresistor with ESP32 and MicroPython.
In the previous posts, I described a simple weather station that measures temperature and humidity and sends the measurements to a Google sheet. The system is supposed to be used at home. Therefore, one of the next possible improvements can be measuring air quality in a room. That can be done, for example, by adding an MH-Z19B sensor for measuring CO2 level. We’ll use again a ESP32 board and MicroPython.
