Seed-Coder-8B-Base — agentic threat model
Seed-Coder-8B-Base is an open-source code generation model family with low inherent agentic autonomy, but it poses software supply-chain risks if poisoned training data or adversarial prompts lead to the generation of insecure or malicious code in downstream applications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| 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.
As a foundation model family trained on public code, it is highly susceptible to adversarial prompt injection, model-reprogramming, and generating insecure code. The open-source nature also makes it vulnerable to model stealing and offline exploitation.
Uses a 'model-centric' data processing pipeline where smaller LLMs filter training data sourced from GitHub and the web. This introduces risks of automated data poisoning, bias propagation from the filtering models, and potential licensing or provenance gaps in the training corpus.
Not certain from the listing — The listing describes raw foundation models rather than an active agentic orchestration framework. If integrated into an agent framework, threats would include insecure tool integration (e.g., executing generated code) and memory poisoning.
Not certain from the listing — As an open-source model, deployment is entirely up to the end-user. Risks depend heavily on whether the model is hosted in a secure, sandboxed environment, especially if it is used to execute or test generated code.
Not certain from the listing — No built-in guardrails, evaluation, or observability mechanisms are detailed in the public listing. Users must implement their own monitoring to detect drift, malicious inputs, or vulnerable code generation.
Not certain from the listing — The model is released under an open-source MIT license with no built-in access controls, identity management, or compliance certifications mentioned.
Not certain from the listing — The model does not natively operate in a multi-agent ecosystem or marketplace, though it could be deployed as a component within one.
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.