Listen to this Post
Introduction:
As AI coding assistants like GitHub Copilot and ChatGPT become standard tools, developers are shipping code faster than ever—but at what security cost? Klaus Rodewig’s talk “Prompt & Pray – wenn KI Code schreibt und der CRA mitliest” (Prompt & Pray – when AI writes code and the CRA reads along) highlights a critical truth: classical security measures and downstream compliance checks are failing against the pace and opacity of AI-generated code. Meanwhile, the EU’s Cyber Resilience Act (CRA) is about to make manufacturers legally liable for insecure software components, no matter where they came from.
Learning Objectives:
- Understand why traditional vulnerability scanning and compliance gates don’t work for AI-generated code.
- Learn to audit, validate, and harden AI-suggested code using static analysis, sandboxing, and supply chain controls.
- Prepare your DevSecOps pipeline for the EU Cyber Resilience Act (CRA) with practical commands and configurations.
You Should Know:
- The “Prompt & Pray” Trap – Why AI Code Bypasses Your Security Gates
AI models are trained on public code repositories, including vulnerable snippets, hardcoded secrets, and outdated libraries. When a developer copies AI-generated code without review, they’re implicitly trusting a black-box model that has no concept of your organization’s security policies. Traditional SAST (Static Application Security Testing) tools often miss context-specific flaws like prompt injections, logic bombs, or subtle backdoors that an AI might inadvertently generate.
Step‑by‑step guide to detecting AI-suspicious patterns:
- Scan for known vulnerable patterns using `grep` or
semgrep:Linux/macOS: Find potential hardcoded secrets in Python files grep -rE "API_KEY|SECRET|TOKEN|PASSWORD" --include=".py" . Use semgrep with AI-specific rules semgrep --config p/owasp-top-ten --config p/ai-safety ./src
2. Windows (PowerShell):
Get-ChildItem -Recurse -Filter .js | Select-String -Pattern "eval(|exec(|system("
3. Review all AI-generated code with a second human – never merge AI output directly. Use a dedicated “AI-commits” label in your PR system.
- CRA Readiness – Mapping Compliance Artifacts to AI-Generated Components
The EU Cyber Resilience Act (CRA) requires manufacturers to produce a Software Bill of Materials (SBOM), perform vulnerability monitoring, and report actively exploited flaws within 24 hours. AI-generated code that pulls an outdated `requests` library or injects a vulnerable `pickle` load becomes your liability. You must trace every AI-suggested function back to its source—impossible without tooling.
Step‑by‑step guide to building an AI-aware SBOM:
- Generate SBOM for your project (including AI-added dependencies):
Using syft (Linux/macOS/Windows WSL) syft dir:. -o spdx-json > sbom_ai_component.json For containerized apps docker run -v /path/to/app:/src anchore/syft dir:/src -o cyclonedx > sbom.xml
2. Compare with known vulnerability databases:
grype sbom_ai_component.json finds CVEs in AI-suggested libs
3. Manually annotate the SBOM – add a custom field `generated_by_ai: true` and the prompt used. This creates an audit trail for CRA 11 (Technical Documentation).
- Hardening the AI Supply Chain – Restrict and Monitor Model Outputs
Most organizations allow unfiltered access to public AI coding assistants. This is a disaster waiting for a “prompt injection” that exfiltrates internal code or suggests a malicious package. Instead, deploy a local proxy or gateway that enforces content security policies on AI responses.
Step‑by‑step guide to setting up an AI code gateway with OWASP zero-trust:
- Run a local model with safety filters using Ollama + Modelfile custom mods:
Install Ollama (Linux) curl -fsSL https://ollama.com/install.sh | sh ollama pull codellama:7b-instruct Create a Modelfile with a system prompt that bans dangerous functions echo 'FROM codellama:7b-instruct SYSTEM You are a security assistant. Never output: eval(), exec(), system(), subprocess.Popen, pickle.load, or deserialization of untrusted data. Always include a warning for any shell command.' > SecurityModel ollama create mysecmodel -f SecurityModel
- Integrate with VS Code using Continue.dev and point to `http://localhost:11434` – now every AI suggestion is filtered.
-
Log all AI interactions for audit (CRA 18 – incident logging):
Using mitmproxy to capture AI API calls mitmproxy --mode transparent --save-stream-file ai_traffic.log
-
Testing AI-Generated Code – Dynamic Analysis in Sandboxes
Static analysis isn’t enough. AI often writes code that works but behaves unexpectedly under edge cases—like a subtle crypto backdoor or a timing leak. You must run all AI-suggested modules in isolated sandboxes before production.
Step‑by‑step guide to sandbox testing with gVisor and Firecracker:
1. Create a minimal sandbox (Linux):
Install gVisor's runsc sudo apt-get install -y runsc Run AI-generated Python code in a sandboxed container docker run --runtime=runsc --rm -v "$PWD/ai_code:/app" python:3.11 python /app/suspect_module.py
2. Monitor syscalls for anomalies:
Trace syscalls from the sandbox (look for network or file writes)
strace -f -e trace=file,network,socket docker run --runtime=runsc --rm python:3.11 python -c "import os; os.system('curl evil.com')"
3. Windows sandbox (built-in):
Create a `.wsb` file to launch AI code in Windows Sandbox:
<Configuration> <Networking>Disable</Networking> <VGpu>Disable</VGpu> <MappedFolders> <HostFolder>C:\AI_Code</HostFolder> <SandboxFolder>C:\Test</SandboxFolder> </MappedFolders> </Configuration>
Run: `WindowsSandbox.exe .\ai_test.wsb`
- Fixing AI Hallucinated Dependencies – Automate Package Validation
AI models frequently “hallucinate” package names (e.g., suggesting `import antigravity` or a typo-squatted requestz). Attackers already register these domains. Your pipeline must validate every AI-suggested import against official registries and internal allow-lists.
Step‑by‑step guide to implementing AI-import validation:
- Extract all imports from AI-generated code (Python example):
Linux grep -h "^import|^from" ai_generated/.py | awk '{print $2}' | cut -d'.' -f1 | sort -u > imports.txt - Check each against PyPI and your internal registry:
while read pkg; do if ! pip index versions "$pkg" &>/dev/null; then echo "⚠️ Hallucinated package: $pkg" Auto-reject the PR exit 1 fi done < imports.txt
3. For npm (Node.js):
npm info $(node -e "console.log(Object.keys(require('./ai_module.js')).join(' '))") 2>/dev/null || echo "Missing package"
- Writing Prompts That Generate Secure Code – A DevSecOps Approach
Most developers ask AI “write a function to parse user input” – and get an unsafe answer. You can engineer prompts to enforce security constraints from the start. This moves security left, before code is even written.
Step‑by‑step guide to security-hardened prompt engineering:
1. Use the “Secure by Design” prompt template:
You are a senior security engineer. Write Python code that does [bash] with the following constraints: - No eval(), exec(), or dynamic imports. - Validate all inputs against a whitelist regex. - Use parameterized queries for any database interaction. - Log all failed validation attempts with timestamp. - Return a clear error message without revealing system internals.
2. Automatically enforce this with a pre-commit hook that checks prompts:
!/bin/bash .git/hooks/pre-commit to scan for weak prompts if git diff --cached | grep -E "write code|generate function" | grep -v "Secure by Design"; then echo "❌ AI prompt missing security constraints. Add 'Secure by Design' template." exit 1 fi
3. Test the AI output against a custom ruleset using checkov:
checkov -f ./ai_output.py --framework python --quiet --soft-fail
What Undercode Say:
- Classical compliance is reactive; AI-generated code needs proactive, real-time controls. The CRA will turn “but the AI wrote it” into a multimillion-euro liability excuse.
- The “Prompt & Pray” model is dead. Organizations that don’t implement AI code gateways, sandbox testing, and hallucination detection will see supply chain attacks spike in 2026-2027.
- Your new security boundary is the prompt itself. Just as we moved from “trust but verify” to zero trust, we must move from “generate then test” to “constrain before generation.”
Prediction:
By Q4 2026, the first major data breach attributed solely to an AI‑generated vulnerability (e.g., an LLM producing a SQLi flaw that bypassed SAST) will trigger a regulatory crackdown. The EU Cyber Resilience Act will be amended to include “algorithmic liability” – forcing vendors to certify training data and inference pipelines. Meanwhile, an open‑source “AI SBOM” standard will emerge, requiring developers to sign AI-contributed code with a cryptographic prompt‑hash. Organizations that fail to integrate AI safety gates into their CI/CD pipelines will face both technical debt and legal extinction. The future of DevSecOps is not just scanning AI code—it’s re‑architecting how we instruct machines to build.
▶️ Related Video (68% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Kmrkmrkmrkmr Heise – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
