← OneSky Localization Agent (OLA)
OneSky Localization Agent (OLA) — agentic threat model
OneSky Localization Agent (OLA) presents a moderate security risk profile; while its actions are limited to text translation and localization, its multi-agent architecture and handling of potentially sensitive pre-release IP introduce risks of data exfiltration and translation poisoning.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.50 | |
| 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 — the specific LLMs utilized are not disclosed. General threats include prompt injection attacks designed to bypass translation instructions, potentially leading to the generation of offensive, brand-damaging, or malicious localized content.
Not certain from the listing — the storage mechanisms for translation memories, glossaries, and customer source files are unspecified. General threats include data poisoning of translation memories to systematically inject malicious payloads or brand-damaging terms into future translations.
The agent orchestrates a multi-agent workflow (translator, editor, reviewer). Threats include orchestration bypass, where malicious inputs exploit the parser or logic of the orchestration framework to skip the review phase entirely.
Not certain from the listing — the hosting environment, API security controls, and sandboxing of file parsers are not detailed. General threats include insecure API endpoints and the risk of remote code execution if the agent parses maliciously crafted localization files (e.g., .xml, .json, .po) without strict isolation.
The system relies on automated quality checks to flag poor translations for human review. A key threat is evaluation gaming, where an attacker crafts a malicious translation that bypasses the auto-flagging heuristics, allowing unauthorized or altered content to be published without human oversight.
Not certain from the listing — compliance certifications (such as SOC 2 or GDPR alignment) are not mentioned. General threats include regulatory non-compliance if sensitive personal data (PII) within source files is transmitted to third-party LLM providers without proper data processing agreements.
OLA operates as a multi-agent ecosystem (translator, editor, reviewer). This introduces agent-to-agent trust abuse, where a compromised translator agent could exploit vulnerabilities in the editor or reviewer agents by passing malformed strings designed to hijack their execution contexts.
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.