Bridging the gap between the speed of AI-assisted development and enterprise-grade security practices
The rapid adoption of AI coding assistants has revolutionized development speed, but often at the cost of security. "Vibe coding" – the practice of using tools like Cursor, Windsurf, Bolt, v0, Lovable, Claude Code, GitHub Copilot, and Gemini to quickly generate entire applications from prompts – prioritizes speed and developer experience over security considerations.
At Vibe Hacking, we're bridging this gap. Our goal is to ensure developers can maintain the velocity and creativity benefits of AI-assisted development while implementing enterprise-grade security practices. We call this approach "vibe hacking" – maintaining the "vibes" of efficient development while "hacking" away at the security vulnerabilities.
Our security assessment tool provides a structured, easy-to-use framework to identify and address critical security vulnerabilities in AI-generated code, mapped to OWASP Top 10, OWASP LLM Top 10 v2025, NIST AI RMF, NIST CSF 2.0, MITRE ATLAS v5.1.0, CSA AI Safety Initiative, and the EU AI Act.
Answer yes / no questions across 13 security domains. Each question is weighted 1–3 by exploitability — critical issues like injection and broken access control count more than best practices. Failing categories unlock a ready-made AI prompt you can paste directly into Cursor, Windsurf, Claude Code, or any other coding assistant.
Restrictions on authenticated users are not properly enforced, potentially allowing unauthorized access to protected data or functionality.
Common in AI-generated code: AI assistants often generate simplified authorization checks that don't account for complex permission hierarchies or resource ownership verification.
Failures related to cryptography that lead to sensitive data exposure through weak encryption, improper key management, or outdated algorithms.
Common in AI-generated code: AI tools may generate code using deprecated encryption methods or implement encryption without proper key management strategies.
Vulnerabilities where untrusted data is sent to an interpreter as part of a command or query, tricking it into executing unintended commands.
Common in AI-generated code: AI assistants frequently generate code that directly concatenates user input into SQL queries, shell commands, or HTML output without proper sanitization.
Vulnerabilities in the authentication system that could allow attackers to assume users' identities or access sensitive functionality.
Common in AI-generated code: AI tools often implement basic authentication systems without consideration for password policies, account lockouts, session management, or multi-factor authentication.
System configuration errors that leave your application vulnerable, including insecure defaults, incomplete configurations, and exposed cloud storage.
Common in AI-generated code: AI assistants rarely include comprehensive security headers, proper error handling, or environment-specific configuration best practices.
Unique security challenges created by AI-assisted development and AI components within applications.
Examples: Prompt injection attacks, over-reliance on AI-generated code without review, and lack of validation for AI outputs used in critical functionality.
Risks introduced through third-party packages and build tooling. AI coding tools freely add npm/pip/cargo packages without vetting their security posture or CVE history.
Common in AI-generated code: AI assistants routinely add unvetted third-party packages, outdated libraries with known CVEs, and never configure dependency scanning or lockfile pinning.
Insufficient logging and alerting allows attacks to go undetected, giving attackers time to pivot and cause far greater damage before discovery.
Common in AI-generated code: AI tools rarely implement structured security event logging, tamper-evident log storage, or real-time alerting on suspicious patterns.
Security risks unique to AI agents and Model Context Protocol (MCP) servers that have persistent access to codebases, filesystems, databases, and external APIs.
New in 2026: As agentic tools like Cursor, Windsurf, and Claude Code gain MCP integrations, prompt injection, over-privileged agents, and unsanctioned autonomous actions have become critical attack surfaces. Attack techniques tracked in MITRE ATLAS v5.1.0.
Regulatory obligations for AI-enabled products are accelerating. The EU AI Act reaches full enforcement August 2, 2026; the EU Cyber Resilience Act begins September 11, 2026.
New in 2026: Covers EU AI Act risk-tier classification, NIST AI RMF mapping, AI Bill of Materials (AI-BOM), AI transparency disclosures (Article 50), and AI-specific incident response planning. Aligned with ISO/IEC 42001:2023.
Every question in the assessment maps to one or more of the following authoritative sources. We keep them updated as standards evolve. See the full category mapping →
The industry-standard list of the ten most critical web application security risks. Forms the backbone of eight of the thirteen assessment categories.
owasp.org/Top10The 2025 revision of OWASP's LLM-specific risk list. Covers prompt injection (LLM01), system prompt leakage (LLM07), training data poisoning, and eight other LLM-specific attack classes. Mapped across the AI-Specific Vulnerabilities category.
genai.owasp.orgNIST AI RMF (January 2023) and NIST AI 600-1 (July 2024 generative AI profile) provide GOVERN, MAP, MEASURE, and MANAGE functions for responsible AI deployment. Mapped in the AI Governance & Compliance category.
airc.nist.govNIST Cybersecurity Framework 2.0 (February 2024) adds a GOVERN function to the original five. SP 800-218A (Secure Software Development Practices for AI and ML) provides supply chain and SDLC guidance specific to AI systems.
nist.gov/cyberframeworkAdversarial Threat Landscape for AI Systems — updated November 2025 with agent-specific TTPs (Tactics, Techniques, and Procedures). The go-to reference for real-world AI attack techniques including model evasion, data poisoning, and prompt injection. Mapped in the Agentic AI & MCP Security category.
atlas.mitre.orgCloud Security Alliance's AI Safety Initiative provides guidance on securing AI workloads in cloud environments, covering data governance, model security, and responsible AI deployment practices.
cloudsecurityalliance.orgThe EU Artificial Intelligence Act establishes a risk-tiered regulatory framework (minimal, limited, high-risk, unacceptable). Full enforcement began August 2, 2026. High-risk AI systems require conformity assessments, technical documentation, and mandatory human oversight. Article 50 mandates transparency disclosures when users interact with AI. Mapped in AI Governance & Compliance.
EU AI Act overviewThe first international management system standard for AI. Provides a framework for establishing, implementing, and maintaining responsible AI governance within organizations. Referenced in the AI Governance & Compliance category alongside NIST AI RMF.
iso.org/standard/8123013 categories. Weighted scoring. Copy-paste AI fix prompts. Runs entirely in your browser.