This past year, we’ve seen tremendous growth and adoption of AI coding assistants, platforms, and tools. We’ve even seen the coining of the phrase “Vibe Coding,” which is often referred to as leaning into AI coding tools and letting them primarily lead development without too much oversight, validation, or rigor.
While there’s no denying that this free-spirited method of development has led to some remarkable experiences in terms of velocity, volume, and output, it also comes with its own risks.
For example, leading research such as BaxBench found that state-of-the-art LLMs still produce code with security vulnerabilities 62% of the time, and roughly half of the correct or functional outputs are insecure. Others have pointed out that the over-reliance on AI coding tools is leading to a situation where new junior developers can’t actually code, as they lose a deep understanding of what they’re shipping or how it works.
Another dilemma is that AI coding tools are primarily trained on large open-source software datasets, which are riddled with vulnerabilities. Some studies have found that 86% of codebases contain at least one vulnerability, and 81% contain high- or critical-level vulnerabilities. Others are pointing out the rise of attackers looking to exploit LLM package hallucinations by creating malicious packages with the same names as those hallucinated by the coding tools.
Of course, this is just the initial activity of code creation. But as developers become more “productive” with vibe coding, this inevitably means more lines of code, faster iterations, and increased development velocity. The problem is that on the back end of this comes the exacerbation of challenges organizations already have with remediation. Studies have shown that organizations only have the capacity to remediate about 10% of the vulnerabilities they encounter, with the others being added to security technical debt and backlogs.
The problems continue to grow out of control, with findings citing large enterprise environments that have vulnerability backlogs of hundreds of thousands, and even millions, of vulnerabilities that have yet to be remediated.
And, this was all before the rise of AI coding tools and platforms.
In a recent detailed write-up on measuring the impact of AI,
demonstrated how leading tech firms, on average, are citing 20%~ improvements in PR’s and velocity, with as high as 90% in adoption rates of AI coding tools, which is a sign of where the rest of the industry is likely to follow suit.VibeSec Enters the Discussion
All of these coinciding factors are driving the need for a new, AI-native security paradigm to keep pace with modern development. That’s why OX Security’s VibeSec caught my attention.
It aligns with the broader change in the software development landscape, where coding tools have matured from simple assistants and co-pilots that provide coding outputs to being empowered to take semi or fully autonomous workflows and activities. This opens up tremendous potential for developers, but, as I advocated in my piece “Security’s AI-Driven Dilemma,” it also offers an opportunity for a complete paradigm shift in security, if adopted correctly.
For example, Cursor has “Agent” mode, which can create complex features, refactor code bases, and conduct autonomous exploration, including all tool usage. Similarly, Anthropic’s Claude Agent Mode can be used via Claude Code to act independently, utilizing built-in or user-added tools and functions to carry out tasks autonomously.
In my piece about security’s AI-driven dilemma, I argued that security needed to pivot from being a laggard and late adopter of emerging technology to being an early adopter and innovator. This VibeSec capability from OX Security addresses exactly that, utilizing LLMs and agents to tackle longstanding security challenges, including friction for developers and their workflows, runaway vulnerability backlogs, and the burden of determining whether remediations will cause breaks in functionality.
OX’s VibeSec approach involves combining several foundational purpose-built capabilities they’re rolling out to equip AppSec practitioners and security teams to keep pace with the new development velocity.
This includes:
OX Mind
AI Data Lake
Environment Mapping
Policy Integration
I’ll take a moment to discuss each of these aspects of their VibeSec platform and its role.
OX Mind
At the core of their VibeSec Platform is OX Mind, a multi-agent system (MAS) that leverages RAG to gather information, such as system and environmental context, to be applied to a project being worked on and relevant threat models. OX Mind is also presented as an interactive chatbot within OX’s Dashboard, where users can ask questions related to applications, vulnerabilities, code changes, and more.
OX’s Mind also includes a “sub-mind”, which helps guide vibe coding agents by providing code-to-runtime context for new code and helps drive backlogs by automating the remediation of vulnerabilities, flaws, and potentially insecure code.
AI Data Lake
OX’s AI Data Lake leverages the team's comprehensive security database. It integrates it with information from live sensors in environments, including context related to the cloud, containers, code, and the runtime environment. All of this aligns with the organization’s specific code and security controls and mitigations.
Environment Mapping
One of the core problems associated with most legacy AppSec tools is that they present findings without environmental context. VibeSec’s environment mapping examines the organization's infrastructure, codebase, and architecture to provide tailored preventative remediations and help prioritize findings based on environmental context, which can help minimize developers' effort and frustration.
Policy Integration
Historically, there’s been a gap between policy and practice, and that’s because policy is akin to a static document sitting in a storage folder somewhere, disconnected from the reality of how developers write code and applications.
The VibeSec policy integration capability allows an organization's security policies to become actionable by being integrated directly into development workflows and enforcing compliance from the earliest phases of the SDLC, making Secure-by-Design more than a catchphrase.
Threat Modeling
Another aspect of VibeSec that I found particularly interesting is how it incorporates trusted security practices, such as threat modeling. Threat modeling has long been considered a core aspect of designing and building secure systems; however, it can also be seen as overly burdensome and impractical to implement in many cases.
VibeSec utilizes several leading threat modeling frameworks to provide a holistic threat-informed approach to development. For example, it employs a multi-threat modeling approach, utilizing PASTA at a high level for threat modeling, STRIDE to categorize threats, and then DREAD to assign probabilities to those threats based on their potential exploitation and impact. All this is done in alignment with the real system context, taking threat modeling from a hypothetical exercise to one that represents an organization’s true risk posture.
Bringing it all together
In the era of agentic coding and exponential developer productivity, OX’s VibeSec platform introduces a new security paradigm, leveraging the same emerging technologies as our development peers, and breaking the legacy model of cybersecurity being a late adopter and laggard. It does all of this while integrating natively with Developers’ workflows and tooling, and bringing Secure-by-Design from rumor to reality.
It helps burn down mounting vulnerability backlogs, minimizes developer toil with context-driven, threat-informed automated remediations and runtime insights, breaking free from the struggling shift-left security model, which has failed to keep up with modern development, even before the agentic era.