AdeptAds — agentic threat model
AdeptAds presents a high-risk profile due to its direct integration with Google Ads APIs, enabling automated campaign creation and daily budget optimization. While the human-in-the-loop approval for daily optimizations provides a critical safeguard, a compromise of the agent's credentials or multi-agent orchestration could lead to unauthorized ad spend and brand damage.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — likely utilizes commercial LLMs and text-to-image models to generate ad copy and images. Primary threats include prompt injection leading to the generation of offensive or policy-violating ad creatives, and model misalignment.
Not certain from the listing — ingests business descriptions, target audience parameters, and daily performance metrics from Google Ads. Risks include data poisoning of performance metrics, which could skew optimization algorithms, and exposure of proprietary business strategies.
Orchestrates a multi-agent workflow to execute keyword research, copywriting, and asset generation. Vulnerabilities include insecure tool integration with the Google Ads API, where a hijacked agent could execute unauthorized API calls to drain ad budgets.
Not certain from the listing — operates as a closed-source SaaS platform. The critical infrastructure risk is the secure storage and handling of Google Ads OAuth tokens; compromise of the backend database would expose client ad accounts to external control.
Not certain from the listing — provides a user interface for approving daily optimizations, but internal logging of agent decisions and API calls is unspecified. Gaps in observability could prevent detection of silent failures or malicious agent behavior.
Relies on Google OAuth for authentication and authorization. Compliance risks are high, as AI-generated ad copy and images must strictly adhere to Google Ads policies and regional advertising regulations to avoid account suspension.
Employs a 'team of AI agents' working collaboratively to build and optimize campaigns. Threats include cascading failures if one agent (e.g., keyword researcher) passes corrupted or malicious inputs to another (e.g., ad copy generator), bypassing safety filters.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).