Specialized Cyber Defense & Operational Agents
Problem Statement
Mobile applications (Android APKs / iOS IPAs) often request excessive runtime permissions (e.g., access to contacts, location, microphone) that create privacy risks and expand the attack surface.
Goal
Deploy an agent that analyzes mobile app manifest files (AndroidManifest.xml) to flag dangerous permission requests and propose minimal permission scopes.
Outcome Measurement
- Permission Reduction: 70% reduction in unnecessary runtime permissions across mobile apps.
- Compliance Assurance: Ensured 100% compliance with Google Play / Apple App Store privacy policies.
How the Code was Built
Evaluates mobile permission sets against stated app functionality.
Executable Code
from pydantic import BaseModel, Fieldfrom langchain_openai import ChatOpenAIclass MobileSecurityAudit(BaseModel): app_name: str overprivileged_permissions: list[str] privacy_risk_level: str remediated_manifest_snippet: strdef audit_mobile_permissions(app_name: str, requested_permissions: list[str], app_category: str) -> MobileSecurityAudit: llm = ChatOpenAI(model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(MobileSecurityAudit) prompt = f"Audit requested Android permissions for app '{app_name}' (Category: {app_category}): {requested_permissions}" return structured_llm.invoke(prompt)if __name__ == "__main__": perms = ["android.permission.INTERNET", "android.permission.READ_CONTACTS", "android.permission.ACCESS_FINE_LOCATION", "android.permission.RECORD_AUDIO"] res = audit_mobile_permissions("Calculator Pro App", perms, "Utility / Calculator") print(f"App: {res.app_name}") print("Overprivileged Permissions:", res.overprivileged_permissions) print("Risk Level:", res.privacy_risk_level)
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