Every day, development teams are generating more code than ever, but a growing portion is now being written by generative AI. Generative AI assistants like GitHub Copilot and Replit  AI and others are writing boilerplate code, refactoring modules, and even generating entire functions. This surge in productivity also brings new risks. 

Traditional application security (AppSec) tools follow a simple cycle: write code, commit, scan, and fix. This model works when humans write the code, scan the repository post-commit, and then security teams triaged. However, when AI starts writing significant portions of the application, that pipeline changes. 

In this article we explore how GenAI changed the software supply chain and created a new “security gap,” that traditional scanners fail to protect. 

How GenAI is Reshaping the Software Supply Chain 

The modern software supply chain now begins at the prompt. In today’s workflow, this developer sequence is almost universal: 

1. A developer prompts their AI assistant. 
2. The model emits code almost instantaneously. 
3. The developer accepts, modifies, and commits. 

While this workflow feels frictionless and boosts velocity, code generation has quietly become the new entry point for risk. 

Vulnerabilities that once emerged downstream in outdated dependencies, open-source packages, or legacy codebases, now originate upstream from creation time. GenAI is known for accelerating coding, but at the same time also accelerates the introduction of logic errors, insecure defaults, and unvetted dependencies that can slip into builds and production. 

Checkmarx’s 2025 State of Code Security Survey found that while 99% of development teams now use AI-assisted coding tools, only 29% have implemented formal AI security controls. In other words, nearly every development team is using AI to generate or modify production code, but only a fraction is properly monitoring how that code is being produced, validated, and reviewed. 

This lack of oversight has redefined the security perimeter. Code that once flowed through structured write and review cycles is now written in seconds by a non-human contributor, without the typical visibility that developers and AppSec teams previously relied on. 

The result is a new class of vulnerabilities: syntactically correct, functionally sound, yet logically unsafe. These flaws compile, pass tests, and even ship to production, but they can violate security policies or expose sensitive logic that scanners miss until it’s too late. 

The Software Supply Chain Has a New Upstream Input 

Traditionally, the software supply chain referred to open-source packages and dependencies entering a build, following a predictable sequence: source, build, dependencies, and deploy. Security programs were designed around this flow – scanning code as it moved downstream, validating open-source components, and monitoring what entered the build. But now, AI-generated code has become a new upstream source in the supply chain. 

The software supply chain now begins long before a build ever runs. Each AI-assisted coding session can introduce a new upstream input that never existed in traditional development models: 

• Code completions and scaffolding created by large language models (LLMs) 
• AI-suggested dependencies and third-party packages added automatically 
• Automated refactoring and template rewrites performed by assistants such as Copilot, Cursor, or Windsurf 

Every one of these elements has become part of the modern software supply chain, but they often bypass manual review and existing AppSec gates. This creates a blind spot at the point of code creation, inside the IDE itself. To close this gap, security coverage must begin earlier and extend all the way into the development environment where GenAI actually operates.  This gap at the creation layer is where the most critical risks now emerge. 

The Emerging Security Gap

Vulnerabilities Slip in Unseen  

AI generated code may follow the correct syntax correctly, but it doesn’t always follow best security practices. Consider this: 

• The AI scaffolds an endpoint with eval() or insecure default configs. 
• It pulls in a package from npm or PyPI with known vulnerabilities because the assistant “thought it matched”. 
• It ignores enterprise-specific security standards because the prompt didn’t specify them. 

These scenarios highlight risk before the code even hits the repository. Checkmarx recognizes that large language models weren’t trained in secure-coding practices and that “slick-looking code” can still collapse under attack. Additionally, typical SAST tools only reviews code once committed, creating a security blind spot. 

Why Post-Commit Scanning Fails 

Traditional AppSec relies on the scan after commit model. In AI-assisted development, that’s like checking the locks after the intruder’s already inside: by the time a SAST or SCA tool runs, the vulnerable logic is merged or possibly even deployed. 

There are several reasons: 

• Latency: By the time a scan runs, the code is merged and maybe deployed. Fixing now is more expensive. 
• Context loss: The prompt and both AI and developer edits often leave no trace. Scans only see the code, not how it was created. 
• AI behavior: Code produced by AI may not map to traditional heuristics; it may use unusual patterns or mix libraries in novel ways. Static pattern scanners may miss these. 

In a recent blog about the IDE as a critical attack surface, we argued that DevSecOps teams must “shift detection to the moment vulnerabilities are introduced.” This shift from reacting  to vulnerabilities after they appear to preventing them as they’re written,  defines the new standard of AI-aware, developer-first security. 

New Attack Vectors: LITL, Prompt Injection, Shadow AI 

As AI becomes a co-author in software creation, the attack surface is expanding in subtle and dangerous ways. Traditional vulnerabilities like SQL injection and buffer overflows haven’t disappeared, but they’ve been joined by new, AI-specific threats that exploit not only how generative systems think, but also how they code. 

• Lies-in-the-Loop (LITL): manipulates how AI coding assistants learn from feedback, injecting unsafe logic that looks valid to both the developer and the model.  
• Prompt Injection: Attackers manipulate inputs to assistants, so they generate insecure code or embed malicious logic.  
• Shadow AI: Developers use unsanctioned assistants (Copilot, Replit AI) bypassing security review – introducing unvetted logic and packages. 

Checkmarx has documented these attack vectors in depth and continues to warn that prevention must start at the coder’s cursor, not after deployment. Defending against these emerging risks means embedding security in real-time, where AI and human developers meet: the IDE. 

The Organizational Risks of “Security Drift” 

When developers use GenAI to generate code but continue choosing tools without the oversight this new workflow requires, the organization faces “security drift.” Practices begin to diverge from centralized policy, often without visibility. 

Consider the data: 60% of organizations say GenAI usage is unapproved, yet still occurs. This means that developers are using unsanctioned assistants that introduce risky dependencies and create AI-generated code that bypass SCA (software composition analysis) and secrets scans. Security teams often only identify these vulnerabilities only after they’re deployed. 

This drift isn’t just technical; it’s cultural. When the speed of delivery outpaces the speed of review, risk becomes embedded. 

Why Traditional AppSec Tools Can’t Plug the Gap 

1. Post-commit scanning is too late
   By the time a scanner runs, the damage is done. Control flow may be established; packages imported, secrets exposed. Fixing becomes more expensive.  
   
2. Tool origin ignorance
   Most tools do not distinguish between manually written and AI suggested code. They treat all code equally, which means patterns unique to AI-generated code (e.g., hallucinated dependencies, repeated boilerplate, prompt-inference flaws) may go undetected.  
   
3. Fragmented workflows
   Development workflows vary from IDE completions, CLI generators, AI notebooks, hybrid teams. Traditional scanners remain focused on repo or CI/CD, not the moment code is written. 

Reframing What Secure Software Development Means 

To fill this security gap, organizations must rethink: 

• When security validation happens (shift far left). 
• Where it happens (inside the editor/IDE, not just commit). 
• Who and what it protects (humans and AI assistants). 

The new development model, dotted with AI suggestions, demands security that understands intent, not just syntax. Validation has to happen during code creation,  regardless of whether it’s written by human or an AI assistant. 

Tools like Checkmarx One Dev Assist address this new reality by analyzing dependencies, secrets, and logic inline, enforcing policy before code ever leaves the IDE. As code is authored, whether by human or AI, an intelligent engine validates intent, dependencies, secrets, API usage in real time. Checkmax One Developer Assist delivers what modern AppSec demands: real-time validation of logic and dependencies, inline fixes, and prevention rather than remediation.  

Action Items for Developers Using AI

Here are actionable steps for engineering leaders and developer teams: 

Audit your use of AI assistants 

• Which tools are being used? Are they sanctioned? 
• Are completions being reviewed or audited? 

Embed security in the authoring phase 

• Choose tools that scan as code is typed or generated, not just at commit. 
• Highlight unsafe API patterns (e.g., eval(), permissive configs) at generation time. 

Extend SCA and secrets scanning into AI-generated code 

• Ensure the platforms you use detect packages brought in via assistant suggestions, validate versions, assess “blast radius”. 

Establish governance for GenAI use 

• Define which assistants are allowed. 
• Track usage, flag unapproved tools (shadow AI). 
• Embed policy enforcement into the workflow. 

Measure uplift and risk reduction 

• Track vulnerabilities found pre-commit vs post-commit. 
• Monitor time-to-remediate (MTTR) for AI-generated code. 
• Use developer productivity metrics to justify investment in new tooling. 

Next Steps 

The software supply chain is no longer just about managing dependencies and deployment pipelines. Application security needs to begin at the cursor; the moment code is generated. GenAI has sped up development, but it also accelerated vulnerability introduction. And that needs to be addressed. 

If your AppSec strategy still relies on post-commit scans, you’ll be stuck chasing issues that are already embedded in your codebase. Security must move upstream by validating intent, dependencies, secrets, and patterns in real time, inside the IDE.  

Further Learning: Read the companion article “Why Context Is the New Code: Building AI-Resilient AppSec From the IDE” to discover a practical framework for securing your applications in this new world.