Enhancing GitHub Security Scanning: Integrating AI Threat Taxonomies Into Your DevSecOps Pipeline

9 min readBy Gurvinder Singh

Enhancing GitHub Security Scanning: Integrating AI Threat Taxonomies Into Your DevSecOps Pipeline

If 45% of AI-generated code introduces security vulnerabilities, what percentage of your organization's codebase was written by AI assistants last quarter — and who is scanning it?

Executive Summary 45% of AI-generated code introduces security vulnerabilities, and existing scanners weren't designed to detect AI-specific attack patterns like prompt injection, tool poisoning, or slopsquatting. This article presents five practical enhancements — from the Arcanum Prompt Injection Taxonomy to automated Nuclei template generation — that harden DevSecOps pipelines against the emerging wave of AI-driven threats. Applicable to any organization using AI coding assistants or deploying AI agent frameworks.

The Problem: Your Security Scanners Weren't Built for This

If you're running Semgrep, Gitleaks, or CodeQL in your CI/CD pipeline, you're already ahead of most organizations. But here's the uncomfortable truth: those tools were designed for a world where humans write code. We no longer live in that world.

With 45% of AI-generated code introducing security vulnerabilities and an 86% failure rate for XSS prevention in AI-produced outputs, the attack surface has fundamentally shifted. Vibe coding — where developers describe intent in natural language and ship whatever the model generates — has created what I call Strategic Shadow IT: production systems that nobody on the security team fully understands, built with code that nobody on the development team fully vetted.

This isn't a theoretical risk. I've been tracking how these failures manifest in real-world MCP (Model Context Protocol) implementations through my MCP Sentinel Scanner project, and the patterns are consistent: AI models reproduce insecure patterns from training data with the same confidence they use to produce correct code. The model doesn't distinguish between a parameterized query and a SQL injection vulnerability — both appear thousands of times in open-source datasets.

What's Changed: The Arcanum Prompt Injection Taxonomy

The Arcanum Security team recently released their Prompt Injection Taxonomy v1.5, which provides the most comprehensive classification I've seen for AI-specific attack vectors. For those of us building scanning tools, this taxonomy isn't just academic — it's a detection blueprint.

The taxonomy identifies five critical dimensions that our scanning repos need to address:

Agent Manipulation targets the tools and APIs that LLMs use to take actions. When your AI coding assistant has command-line access or can query internal systems, a successful prompt injection transforms it into an attacker's proxy. A documented case showed a researcher chaining TOCTOU, prompt injection, and poisoned pipeline execution to steal repository secrets from GitHub Copilot Agent — simply by creating a legitimate-looking issue.

Indirect Prompt Injection exploits the gap between trusted and untrusted data. If your AI-powered issue manager processes an external bug report containing embedded instructions, those instructions can hijack the model's behavior to exfiltrate secrets. This is particularly dangerous because the malicious payload never touches your code directly — it enters through productivity tools, email, slides, or even image alt-text.

Ecosystem and Supply Chain Attacks manifest as SSRF and Blind XSS mediated through the model itself. LiteLLM was found vulnerable to an SSRF flaw that exposed OpenAI API keys when the chat/completions endpoint was manipulated to send requests to internal URLs. Your scanning tools need to detect these model-mediated vulnerabilities, not just traditional injection patterns.

Data Layer Attacks target the integrity of RAG knowledge bases and persistent data stores. Poisoning attacks corrupt the knowledge base to ensure consistently incorrect responses — undermining business decisions without triggering any traditional security alerts.

Model Safety and Liability covers jailbreaking, prompt secret extraction, and the generation of unauthorized professional advice. These create legal exposure that most organizations haven't accounted for.

Practical Enhancements for Your Scanning Repos

Based on this research, here are the specific enhancements I'm implementing in the MCP Sentinel Scanner repository.

1. Integrate the sec-context Anti-Pattern Library

Arcanum's sec-context project distills 150+ sources into a set of AI code security anti-patterns that serve as a detection reference. The priority integration order, ranked by detectability and risk:

| Priority | Anti-Pattern | Why It Matters | |----------|-------------|----------------| | 1 | Dependency Risks (Slopsquatting) | AI suggests non-existent packages; attackers register them with malware | | 2 | XSS Vulnerabilities | 86% failure rate in AI-generated code | | 3 | Hardcoded Secrets | Scraped within minutes of exposure to public repos | | 4 | SQL Injection | Prevalent insecure patterns in training data | | 5 | Authentication Failures | 75.8% of developers trust AI-generated auth logic without vetting | | 6 | Missing Input Validation | Root cause enabling most injection attacks | | 7 | Command Injection | Real-world RCE demonstrated through AI-generated code | | 8 | Missing Rate Limiting | High frequency across AI-produced APIs |

For organizations using models with large context windows (Claude Sonnet or GPT-4 Turbo), the entire ANTI_PATTERNS_BREADTH.md file (~65K tokens) can be included as a reference file. This anchors the model's behavior with clear BAD/GOOD examples during the generation phase.

2. Deploy a Security Review Agent

The highest-impact enhancement is a standalone Security Review Agent that sits between AI code generation and your commit pipeline. This agent takes AI-generated code as input, scans it against the 25+ documented anti-patterns, and returns specific vulnerabilities with remediation steps.

The critical insight here: you need both the AI-powered review and deterministic validation from SAST tools. Modern multi-agent loops integrate Semgrep and Gitleaks into their quality validation pipelines to catch "ghost rules" — security patterns that the AI claimed to apply but didn't actually implement. Trust but verify.

3. Add AI-Specific Vulnerability Probes

Traditional scanners don't test for model-specific weaknesses. Two tools fill this gap:

NVIDIA's Garak offers 40+ probe modules for hallucination, data leakage, prompt injection, and misinformation. Use it to establish a security baseline for any fine-tuned models before production deployment.

Microsoft's PyRIT focuses on multi-turn attack orchestration — simulating persistent, sophisticated adversarial interactions that simple scanners miss. The PyRIT SHIP Burp Suite extension bridges AI safety testing with traditional web application penetration testing.

4. Automate Nuclei Template Generation

Here's a force multiplier: use AI to generate Nuclei templates for newly disclosed vulnerabilities. Instead of spending 2-4 hours manually writing a detection template, describe the vulnerability to an agentic AI layered with custom rules and a curated template repository. Working multi-request templates with matchers and extractors can be produced in roughly 30 seconds.

This capability lets you fill gaps in commercial vulnerability management scanners and rapidly deploy custom checks for internal business logic flaws. Combined with ProjectDiscovery Cloud for continuous monitoring, you get real-time visibility into exposed admin panels, logging interfaces, and misconfigured infrastructure.

5. Harden Against Shadow IT Discovery

Attackers use public Certificate Transparency logs to identify hidden systems, querying for keywords like "git," "backup," and "logging." Research suggests approximately 30 million hosts are discoverable through simple keyword probing of public records. Your scanning pipeline should include:

  • Monitoring CT logs for your organization's domains
  • Verifying that Kibana, Elasticsearch, and Solr panels require authentication
  • Checking that Git history is cleared before repositories are publicized
  • Validating that all AI-suggested packages are legitimate (anti-slopsquatting)

The Hallucination Factor

Any discussion of AI-augmented security scanning must acknowledge the reliability challenge. Current hallucination rates range from 37% (Llama-3.1-70B-Instruct) to 81% (Mistral-7B-Instruct-v0.3) on benchmarks, with an average of 31.4% across real interactions — spiking to 60% in challenging technical domains like mathematics.

This reinforces a non-negotiable principle: human-in-the-loop and automated validation layers are not optional. Context engineering — providing 10+ input-output examples, clear JSON schemas, and structured prompts — makes models more than twice as effective. Without it, your AI security tools are generating noise, not signal.

Implementation Roadmap

For teams looking to adopt these enhancements, I recommend this phased approach:

Phase 1 (This Week): Integrate Gitleaks and Semgrep into your CI/CD pipeline if you haven't already. These are table stakes.

Phase 2 (This Month): Deploy the sec-context anti-pattern reference as a guardrail for AI code generation. Build or adapt a Security Review Agent.

Phase 3 (This Quarter): Add Garak and/or PyRIT to your pre-deployment red teaming workflow. Begin monitoring CT logs for shadow IT discovery.

Phase 4 (Ongoing): Implement automated Nuclei template generation for new vulnerabilities. Establish continuous feedback loops from bug bounty findings into your scanning pipeline.

What's Next

I'm actively implementing these enhancements in the MCP Sentinel Scanner repository. The goal is to create scanning tools that are purpose-built for the AI-augmented development era — not retrofitted from a pre-LLM world.

The MCP Sentinel Scanner's seven-layer detection pipeline already provides comprehensive coverage for MCP-specific vulnerabilities. The next version will incorporate the Arcanum taxonomy as a native detection framework, adding coverage for agent manipulation, indirect injection, and supply chain attacks specific to MCP implementations.

If you're working on similar challenges, I'd welcome collaboration. The security community moves faster when we share detection patterns openly.

Your next move: Integrate Gitleaks and Semgrep into every CI/CD pipeline this week. Deploy the sec-context anti-pattern library as an AI code generation guardrail this month. Begin red-teaming your AI deployments with Garak or PyRIT this quarter.

What to tell your board:

  • 45% of AI-generated code contains security vulnerabilities, and your existing scanners cannot detect AI-specific attack patterns like prompt injection or tool poisoning
  • Immediate action: integrate Gitleaks and Semgrep into CI/CD pipelines this week; deploy AI code anti-pattern guardrails this month
  • Organizations that delay will face an expanding attack surface as AI coding assistants become standard developer tooling

Research by Gurvinder Singh, CISSP, CISA — Security Researcher and Advisor. Independent security research at SecurityLeader.ai specializing in AI security, MCP vulnerability analysis, and enterprise cyber risk.

References & Further Reading:

Tags

AI SecurityDevSecOpsPrompt InjectionSupply Chain SecurityGitHub SecurityMCP Security