✅ AI Systems Vulnerability Checklist By CyberDudeBivash — AI Security Researcher & Cybersecurity Expert
🧠 I. Model Layer Vulnerabilities
| 🔍 Vulnerability | ✅ Checklist Item |
|---|---|
| 🔓 Prompt Injection | ☐ Sanitize user input before forwarding to the LLM ☐ Use role-based instruction layering ☐ Implement strict output filtering |
| 🐍 Jailbreaking | ☐ Apply adversarial testing with jailbreak prompts ☐ Fine-tune models with safe completion constraints |
| 🔎 Model Inversion | ☐ Apply differential privacy mechanisms ☐ Limit query exposure per user/IP |
| 🧬 Membership Inference | ☐ Enable data de-identification ☐ Obfuscate confidence scores or remove them altogether |
| 🧟♂️ Adversarial Examples | ☐ Use adversarial training during model fine-tuning ☐ Deploy input sanitization and transformation pipelines |
📦 II. Data & Training Pipeline Risks
| 🛠️ Vulnerability | ✅ Checklist Item |
|---|---|
| 🧪 Data Poisoning | ☐ Validate and cleanse training datasets ☐ Maintain a version-controlled and auditable dataset lineage |
| 📉 Label Flipping | ☐ Perform manual + automated label audits ☐ Use anomaly detection on labeled samples |
| 🎯 Backdoored Models | ☐ Verify model checkpoints from trusted sources ☐ Scan for hidden triggers in pretrained weights |
| 📤 Shadow Training | ☐ Obfuscate or encrypt proprietary datasets ☐ Monitor for outbound data exfiltration during training |
🧩 III. API & Integration-Level Threats
| 🔗 Vulnerability | ✅ Checklist Item |
|---|---|
| 🔑 API Key Exposure | ☐ Store API tokens securely (env vars / secrets managers) ☐ Never expose keys in frontend JavaScript |
| 🧱 Lack of Rate Limiting | ☐ Apply API throttling (IP/user/request-based) ☐ Monitor abnormal request patterns |
| 🔌 Third-Party LLM Plug-ins | ☐ Scan plug-in metadata and code for injection points ☐ Sandbox plugin execution environments |
| 🕸 Unrestricted Tool Usage | ☐ Define allow/deny lists for tools used by AI agents ☐ Disable filesystem/network-level access when not needed |
🔐 IV. Deployment & Inference Threats
| 🚀 Vulnerability | ✅ Checklist Item |
|---|---|
| 🧬 Autonomous Agent Exploits | ☐ Sandbox agent behavior ☐ Prevent unsupervised command execution ( eval, exec, subprocess) |
| 👤 Impersonation & Deepfakes | ☐ Use digital watermarking or fingerprinting ☐ Deploy detection AI to monitor generated media |
| 📈 Overexposed Inference Endpoints | ☐ Authenticate API calls ☐ Rate-limit public access points ☐ Encrypt communication channels (HTTPS/TLS) |
| 🧪 Model Drift & Stale Weights | ☐ Regularly retrain and evaluate model accuracy ☐ Track inference performance with versioning |
| 🧩 Insecure Model Serialization (Pickle, ONNX) | ☐ Avoid insecure formats like pickle in production☐ Validate deserialized objects before use |
🛠️ V. Supply Chain & Ecosystem Risks
| 🔐 Vulnerability | ✅ Checklist Item |
|---|---|
| 🐍 Python Package Attacks (typosquatting) | ☐ Audit all third-party libraries ☐ Use dependency lock files ( requirements.txt, pip freeze) |
| 🧳 Compromised Pretrained Models | ☐ Check for known malicious weights (scan SHA256 hashes) ☐ Store in secure object storage or on-prem |
| 🧾 Lack of SBOM (Software Bill of Materials) | ☐ Generate SBOMs using tools like Syft ☐ Monitor for vulnerability disclosures in packages used |
| 🌐 Malicious GitHub Integration | ☐ Avoid executing scripts from unknown repos ☐ Use signed commits for trusted model repos |
🚨 VI. Logging, Monitoring & Response
| 🧠 Defense Domain | ✅ Checklist Item |
|---|---|
| 📊 Prompt & Output Logging | ☐ Obfuscate sensitive data in logs ☐ Monitor logs for injection signatures |
| 🔎 Model Usage Monitoring | ☐ Alert on abnormal queries (length, frequency, content type) ☐ Use AI-specific SIEM dashboards |
| 🧯 Incident Response | ☐ Develop an LLM-specific incident response playbook ☐ Define rollback strategies for corrupted models or agents |
| 🕵️♂️ Red Team Testing | ☐ Conduct regular adversarial testing on AI systems ☐ Include LLM & agent security in pentesting scope |
📌 BONUS: Top Tools for AI Security Testing
| Tool | Function |
|---|---|
PromptInject | Prompt injection testing framework |
SecLLM | LLM vulnerability scanner |
Adversarial Robustness Toolbox | Adversarial sample crafting |
OpenAI Eval | Evaluate model behavior and security |
ModelScan | Model file malware scanner |
TruffleHog | API key exposure scanner |
Bandit | Python AI code static analyzer |
🧠 Final Words from CyberDudeBivash
“Every AI innovation introduces a new threat vector. AI Security isn't optional—it's existential.”
If you're serious about AI, you must treat your LLMs, agents, pipelines, and APIs like any other critical infrastructure—with layered security, continuous monitoring, and zero trust assumptions.
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