Tampering Detection

Introduction to Tampering Attacks

Tampering attacks are different from the exploitation of traditional vulnerabilities. This includes injecting malicious code into the CI/CD pipeline that would embed an exploitable code into the final build. When the build is released into a production environment, it is exploited and threat actors can cause damage to the application. These attacks are more sophisticated to carry out as well as to detect by organizations. Traditional SCAs are incapable of detecting these kinds of attacks, which makes modern cyberspace much more susceptible to these attacks.

Detecting Tampering Attacks

Detecting tampering attacks is much more than finding vulnerabilities with CVE scores which are publicly disclosed and known prior to scans. Hence, it’s necessary to have a robust system that would detect these kinds of maliciously injected code patterns and stop them from entering the production environment.

In these cases of novel attacks, reducing false positives and false negatives is also necessary and can pose a different problem while hunting for malicious code patterns. Myrror Security uses Artificial Intelligence to identify tampering attacks by matching patterns that can lead to any security issues as well as checking the integrity of the external dependencies that are used in the production environment.

Integrity Analysis with Artificial Intelligence

Myrror Security uses Artificial Intelligence and Machine Learning models that are adaptive to particularly given projects and improve with data by learning about possible attacks. Also, AI is much more capable of finding similarities between the compiled code and the source code. This allows Myrror to efficiently and accurately compare the source code of the dependency as well as the compiled package that is pushed in the production environment.

Mechanism of Myrror Security for Detecting Tampering Attacks

Since Myrror Security heavily relies on Reachability Analysis, it moves ahead with Static Code Analysis of the application’s codebase and analyzes it to find the use of dependencies that are used. For analyzing dependencies, Myrror dissects compiled dependencies that are fetched from the package managers, since this is the way that dependencies make their way into the final production build. Myrror uses Binary to Source Code Analysis to develop a graph of all the functions and paths in the dependencies. Here, a layer of AI is introduced to scan for tampering with these dependencies. With BST, the compiled build is compared with the source code with AI and any kind of tampering patterns are identified. AI provides developers with similarity scores and uses this metric to confirm the tampering.

Identifying tampering-related attacks requires robust systems that can detect novel ways of code modification and since these attacks can happen anywhere in the development pipeline, continuous monitoring with automated security solutions is vital to ensure that these code injections don’t reach production builds.