Sentry django-access-review — agentic threat model
This agent acts as a static code analysis skill with read-only access to Django codebases, presenting low agentic risk due to its lack of write permissions, execution capabilities, or autonomous tool usage.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.30 |
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 — relies on an unspecified underlying foundation LLM to parse code and apply heuristics; vulnerable to prompt injection if malicious code comments are crafted to mislead the analysis.
Reads the target Django codebase as its primary data input. Risk of source code exposure or exfiltration if the agent's context window or output channel is compromised.
Orchestrates code reading and heuristic matching. Vulnerable to insecure tool integration if the file-reading tools do not restrict path traversal outside the target Django project directory.
Not certain from the listing — requires secure sandboxing when deployed to ensure that reading untrusted codebases does not lead to local file system compromise or host-level access.
Not certain from the listing — requires robust logging of accessed files and generated vulnerability reports to prevent silent failures or missed authorization gaps (false negatives).
Acts as a security compliance tool itself, but must be governed by strict read-only access policies to ensure it cannot modify the codebase it is auditing.
Operates as a single-purpose Agent Skill. Minimal ecosystem risk unless integrated into a larger CI/CD multi-agent pipeline where its findings trigger automated pull requests.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).