Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

“72% of organizations use AI in business functions — but only 13% feel ready to secure it.” That gap, between adoption and preparedness, explains why traditional AppSec approaches aren’t enough.

Over this last year, we’ve seen significant attacks like the Shai-Hulud worm, the Nx build system compromise, and secrets being leaked to public GitHub Actions logs via the tj-actions/changed-files compromise, but I could spend the entirety of this article only listing different attacks, let alone talking about them.

Application Security Testing (AST) services use automated tools and manual techniques to find and fix security vulnerabilities in software, integrating security into the entire development lifecycle (SDLC) to prevent threats and protect applications from attacks. Key services include Static Application Security Testing (SAST) for code-level analysis, Dynamic Application Security Testing (DAST) for runtime testing, and Interactive Application Security Testing (IAST) which combines both.

Unlike closed-source code or proprietary applications, open source software (OSS) exposes its source code, allowing anyone to view, modify, or contribute to it. This transparency delivers both opportunities and unique threats; developer communities can uncover flaws faster, but attackers can also examine code for weaknesses and even easily leverage known reported open source vulnerabilities.

Today, Mend.io is expanding its AppSec capabilities to secure the five most popular agentic IDEs — including Windsurf, CoPilot, Claude Code, Amazon Q Developer, and Cursor — ensuring that developers can move at AI speed without compromising security.

Containers have transformed how modern applications are built and deployed. They’re lightweight, portable, and allow teams to move software from development to production faster than ever before. But as adoption has accelerated, so have security concerns. From vulnerable base images to exposed Kubernetes clusters, container security has become a top priority for AppSec and DevSecOps professionals.

Code scanning is the process of automatically analyzing source code to identify potential security vulnerabilities, bugs, and other code quality issues. It’s a crucial part of secure application development, helping teams detect and fix problems early in the software development lifecycle. Code scanning tools mainly use static analysis methods (examining code without running it), in contrast to dynamic analysis tools which analyze applications while they are running.

The software security landscape is evolving faster than ever, and AI is accelerating this change. As generative and embedded AI become core to how software is developed, tested, and deployed, security must adapt to protect an entirely new layer of risk. At Mend.io, we’ve spent the past year reimagining what Application Security Testing (AST) looks like in this new reality.

Large language model (LLM) security refers to the strategies and practices that protect the confidentiality, integrity, and availability of AI systems that use large language models. These models, such as OpenAI’s GPT series, are trained on vast datasets and can generate, translate, summarize, and analyze text. However, like any complex software component, LLMs present unique attack surfaces because they can be influenced by the data they process and the prompts they receive from users.

SAST (Static Application Security Testing) tools are crucial for DevSecOps, enabling automated code analysis to identify vulnerabilities early in the development lifecycle. They analyze source code without execution, detecting issues like SQL injection, XSS, and buffer overflows. Popular SAST tools used by DevSecOps teams include Mend, Checkmarx, Snyk, Veracode, BlackDuck, SonarQube, and Semgrep. Integrating SAST into CI/CD pipelines ensures continuous security checks as code is developed.