LedgerMind — agentic threat model
LedgerMind presents a high agentic risk profile due to its deep integration via client-side hooks and access to sensitive tool outputs, file reads, and script executions. While its immutable Git-based audit trail provides excellent observability, its autonomous memory self-healing and context injection capabilities could be leveraged for persistent prompt injection and data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.80 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 1.00 | |
| Contextual Awareness | 0.90 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.40 |
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 — LedgerMind acts as a memory core integrating with external models like Gemini and Claude, meaning foundation model vulnerabilities like prompt injection or adversarial examples depend on the host LLM's robustness.
LedgerMind uses a hybrid SQLite and vector store for memory. Threats include memory poisoning, unauthorized context injection, and data exfiltration of sensitive logged tool results or file reads.
The framework relies on client-side hooks to intercept prompts and inject context. Vulnerabilities include insecure hook execution, memory poisoning during autonomous conflict resolution, and manipulation of the self-healing logic.
Not certain from the listing — Deployment appears to be local/client-side via CLI or desktop integrations, meaning infrastructure security relies heavily on the user's local machine sandboxing and file system permissions.
LedgerMind features strong observability with automatic logging of tool results/actions and an immutable Git-based audit trail, which mitigates blind spots but could be targeted for log tampering if Git credentials are compromised.
Not certain from the listing — While it provides an immutable Git audit trail for compliance, there is no explicit mention of access control, encryption at rest for SQLite, or formal compliance certifications.
As a horizontal memory core, it interacts directly with other agents/frameworks. A compromise in LedgerMind's memory could propagate poisoned context across multiple integrated agents or tools.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.