Emerging / AI-Powered Threatshigh
Data Poisoning Attacks
Adversaries are manipulating data inputs and training sets to skew AI outputs, trust signals, and downstream decisions.
Stat: Corrupting AI training data to manipulate outputs
Read Full BriefcriticalLast updated: June 2026
Vibe-Coded / AI-Built App Vulnerabilities
AI-generated code is accelerating delivery, but unreviewed output is also introducing authentication, data exposure, and dependency risk.
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What\'s happening
Teams are shipping AI-assisted features faster than review processes are adapting.
Generated code often looks plausible but can contain insecure defaults, missing authorization, and flawed input handling.
The risk compounds when generated code is copied between projects without security review.
Who it targets
Startups moving quickly with AI coding tools
Internal tooling teams
Agencies building client apps under tight deadlines
Real-world examples
Public data endpoints left unprotected by copied patterns.
Secrets handled insecurely in generated server code.
Dependency additions introduced without proper provenance review.
What it costs
Security debt grows invisibly behind fast delivery metrics.
Incidents damage trust in both product quality and engineering governance.
Remediation often requires sweeping review of similar generated patterns.
How to protect against it
Add security review to AI-assisted development workflows.
Use static analysis and dependency hygiene by default.
Threat model auth and data access on every generated feature.
Treat generated code as draft output, not production truth.
Vendors
Vendors who specialize in this threat
Atlas Cloud Guard
Cloud Security & CSPM
Cloud posture visibility with policy drift alerts and remediation workflows.
Find More Vendors for This ThreatCobalt Trace
SIEM & Threat Intelligence
Centralized telemetry, alert triage, and analyst-ready threat correlation.
Find More Vendors for This ThreatRelated
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