Application Security | Breaking Cybersecurity News | The Hacker News

As organizations deploy AI coding agents across large monorepos and microservices environments, a fundamental problem emerges: the model may be capable of making the change, yet still lack the organizational context required to make the right change safely. A developer can ask an AI coding agent to deprecate an API field, update an authentication flow, or modify a service interface. The agent can inspect the code available on the developer's machine and search for references. What it may not know is that the field is consumed by four other services across separate repositories, that one of those services belongs to another team, or that the same field eventually carries sensitive data into a third party integration. This is not simply a context window problem. It is a code context problem: providing AI agents with accurate, current, organization wide evidence about how software actually behaves. One emerging approach is to generate that evidence directly from source code us...

Many AI systems depend on input signals that teams cannot fully inspect or explain. These opaque sources reduce visibility into the data paths that influence model behavior. Engineers lose provenance records, limiting the diagnosis of abnormal outputs. This complicates the work of security teams that need clear records of what influenced a model at any point in time. Verifiable search data offers a stable alternative. It gives teams an input they can examine, store, and reproduce in controlled conditions. Engineers can compare model behavior against information that was publicly accessible at the time a result was produced, rather than depend on hidden internal signals. This article outlines why verifiable search data gives AI and security teams the clarity required to maintain operational control. Why Traceability Matters in AI Systems Traceability lets teams follow an input from its origin through each processing step. When every stage can be inspected, engineers can review...

Artificial intelligence is everywhere now. From automated code completion to autonomous infrastructure management, AI tools and AI agents help DevOps speed up deployment cycles and change how development teams operate in general. At the same time, this rapid adoption of AI has created a reality that is hard for security teams to ignore: as with the growth of AI capability within the software development life cycle, the attack surface also grows. In 2025, there were 68 AI-related incidents recorded across major DevOps platforms according to the 2026 DevOps Threats Unwrapped Report . In the first half of 2026, the number of AI-related incidents visibly grew — research from GitProtect Lab tracked 84 AI-related incidents in six months alone. Thus, comparing the first half of 2026 to the same period in 2025 shows that AI-related incidents in development environments have nearly tripled. What do DevOps and DevSecOps say about AI incidents in general? According to GitProtect Lab 's surve...

Penetration testing is intended to help organizations identify weaknesses before attackers can exploit them. Once testing ends, findings must be documented, reviewed, formatted, delivered, assigned, tracked, remediated, and eventually retested. In many organizations, each of those steps happens in a different system and depends on a manual handoff. Testers work in one set of tools. Reports are assembled in Word or spreadsheets. Findings are delivered through PDFs. Security teams recreate them in ticketing systems. Engineering teams update remediation status somewhere else. Retesting is coordinated through email or meetings. By the time the right owner receives the information needed to act, days or weeks may have passed. At PlexTrac , we see this as one of the largest operational gaps in modern offensive security: organizations have invested in finding vulnerabilities, but the process surrounding the pentest has not kept pace. The next phase of pentest modernization is removin...

We had an enterprise customer tell us their entire AI footprint was Copilot. That was the whole answer. One tool, one line item, done. We ran the first scan. Copilot wasn't even close to number one. Claude was. OpenAI came in second. Copilot was third. Nobody on the security team knew because Claude doesn't show up the way a SaaS app used to show up. There's no single login screen, no single admin console, no one place to look. That's the part most security teams miss. Claude isn't one surface. It's six. The six places Claude actually runs Claude Enterprise and Connected Apps. This is the surface everyone pictures: employees typing into Claude, OAuth'd into Google Drive, GitHub, Slack, and Jira, asking Claude to act on what's inside. The audit log shows that a connection happened. It does not show what got pulled into the prompt or what came back out. A finance analyst can drop a quarter of board materials into a conversation in ten second...

Security is fundamentally about knowing what a system is supposed to do, then catching it when it does something else. For software with deterministic execution paths, that is a tractable problem. For AI agents, it is not. An agent does not follow a fixed code path. It reasons toward a goal, selects tools based on that reasoning, and adapts its next action based on what those tools return. The same input can produce a different sequence of actions depending on context, session history, and what an external tool happened to return. Behavior is the attack surface, and behavior changes. Traditional proxies and AI firewalls were built to inspect content: what a user sent, what a model returned. Intent security asks a different question: is this agent doing what it was built to do, in this context, for this user, right now? Answering that requires building a behavioral baseline for every agent and measuring deviation from it continuously. That is the problem Lasso was built to solve....

Attackers prefer the path of least resistance. Why break in when you can log in? That is what makes working credentials so valuable. A leaked password, token, or API key does more than reveal sensitive data; it offers a way in. No vulnerability or privilege escalation chain necessary. If the credential is valid, the attacker can simply use it, and the session will look like ordinary activity. This shifts the questions from "where can attackers break in?" to "where do usable keys tend to accumulate? Increasingly, the answer is the developer laptop: one of the most instrumented machines most companies own, and one of the easiest places for credentials to go unnoticed. The endpoint is watched for malware, behavior, posture, and configuration. But a valid plaintext credential is something else: a door an attacker may not need to force. A fully patched fleet can still have usable keys scattered across its devices, enough to turn one foothold into something much larger....

Why Authentication Context Matters Most people think logging in is a small act. Type your password, type your code, tap a screen, scan a face, and move on. But to the systems on the other side, the method behind that moment can matter just as much as the fact that it happened at all. That is where two strangely named but surprisingly important identity concepts enter the story: OIDC's AMR and SAML 2.0's AuthnContext. They sound like the kind of acronyms that only standards committees could love, yet both were created to answer a deeply human question in digital form: How sure a system has to be before it trusts someone? The backstory starts with the internet growing up. Early online services often treated authentication as a light switch: either the user was in, or the user was out. But as online systems began handling payroll, health records, taxes, academic data, contracts, and financial approvals, that simple model started to crack. A login backed by a reused password is not ...

Most application security (AppSec) teams know their OWASP Top 10, the industry-standard list of the most critical software security risks. Fewer know which of those categories their organization actually fixes. In conversations with security teams, I hear the same story: "We prioritize criticals, so the important stuff gets handled." The data tells a different story. Fix rates vary dramatically by OWASP vulnerability class, and not in the ways most teams expect. The data comes from Semgrep's Remediation at Scale report , which analyzed anonymized remediation patterns across 50,000+ repositories and hundreds of organizations during 2025. The methodology is straightforward: group organizations into two cohorts by fix rate (top 15% as "leaders," remaining 85% as "field"), then compare what each group actually does differently. The gap between leaders and the field isn't about detection quality or prioritization frameworks. Both cohorts apply the s...

While AI reduces some coding flaws, credential sprawl accelerates, expanding the non-human identity attack surface, and making remediation the new security bottleneck. AI is changing software development faster than most security teams can adapt. As coding assistants and autonomous agents become embedded in daily workflows, many assume traditional application security controls will steadily lose relevance. If machines can scan code, catch flaws, and even suggest safer alternatives in real time, then software risk should start to shrink. But that's not what is happening in the real world, according to GitGuardian's security research. The battle isn't in the code anymore, because AI is shifting where the control point is. It's in the credentials, tokens, service accounts, and machine identities that AI systems need in order to access data and take action. This matters because the attack surface has fundamentally changed. AI-assisted commits grew exponentially in 2025 and leaked secr...

When Shai-Hulud 2.0 hit in late 2025, it was a brutal, expensive wake-up call for DevSecOps teams. It showed that the industry's direction of shifting left, where teams pass security onto developers, wasn't the silver bullet everyone hoped for. Pushing that responsibility was fine in theory, but it crumbled quickly because the foundation it was built on was inherently flimsy. As we move further into 2026, we need a more definitive fix to the structural weakness in the pipelines in light of a potential Shai-Hulud 3.0. A major lesson from 2.0 was that internal CI/CD runners were easily hijacked and turned into attack botnets. Teams need to take that finding and come back with a truly proactive defense. A curated catalog is a way for security teams to control exactly what code and components enter their environment, while still giving engineering teams a fast, secure way to build - it is the key to creating a sustainable solution. More on a curated catalog later. The Anatomy o...

AI-powered coding assistants now play a central role in modern software development. Developers use them to speed up tasks, reduce boilerplate snippets, and automate routine code generation. But with that speed comes a dangerous trade-off. The tools designed to accelerate innovation are degrading application security by embedding subtle yet serious vulnerabilities in software. Nearly  half of the code snippets generated by five AI models contained bugs that attackers could exploit, a study showed. A second study confirmed the risk, with nearly one-third of Python snippets and a quarter of JavaScript  snippets produced by GitHub Copilot having security flaws . The problem goes beyond flawed output. AI tools instill a false sense of confidence. Developers using AI assistance not only  wrote significantly less secure code than those who worked unaided, but they also believed their insecure code was safe, a clear sign of automation bias. The Dangerous Simplicity of AI-...

The Perfect Recipe for Endpoint Security Calls for Privilege Control Today's most effective ransomware attacks don't require malware; they require a login. Modern threat actors don't need to break in. They can leverage legitimate identities and their privileges to gain a foothold, then continue to capitalize on them, moving laterally to probe for more opportunities and manipulate vulnerabilities and exploits to spread ransomware and spyware. A vulnerable identity or account tied to an endpoint can quickly become an attacker's ticket to your most valuable assets and controls.  With legitimate identities being used as the initial foothold in more attacks, we're seeing less 'anomalous' activity and far more seemingly normal actions performed by a trusted, privileged user. And attackers are keenly aware of how easily they can 'hide' behind these legitimate user accounts.  This is why Endpoint Detection and Response (EDR) is really only one piece of the endpoint protection puzz...

Identity-based attacks are the #1 cause of breaches, often exploiting weaknesses in traditional identity platforms. It's time for a proactive approach that addresses these gaps and stops threats before they strike. Identity has become the primary attack surface in cybersecurity. According to Forbes, 75% of cyberattacks leverage identity-based threats. Threat actors gain access using stolen credentials, compromised devices, and deepfake impersonation techniques, often bypassing traditional defenses without detection. Many identity platforms rely on MFA, such as push notifications and one-time passcodes (OTPs), which were once considered secure but are now frequently exploited through phishing, MFA fatigue, and man-in-the-middle attacks. The rise of generative AI has made these threats more effective and more prevalent.  To compensate, organizations have deployed tools like Endpoint Detection and Response (EDR), Network Detection and Response (NDR), and Identity Threat Detection ...