SWE-1 ai coding model — agentic threat model
SWE-1 presents a high-risk profile due to its deep integration into developer workflows and multi-file editing capabilities, which could be exploited to inject malicious code or backdoors if the agent is compromised or manipulated via prompt injection.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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.
SWE-1 is a closed-source model family (SWE-1, lite, mini) with advanced reasoning. Primary threats include model stealing, adversarial prompt injection leading to malicious code generation, and mis-aligned outputs.
Not certain from the listing — details on training data, vector stores, or RAG mechanisms for project context maintenance are not specified. Threats include project codebase data exfiltration or poisoning of the context window.
Orchestrates multi-file editing, bug fixing, and tool use. Threats include tool misuse (e.g., executing destructive commands or corrupting files) and insecure tool integration within the developer's local environment.
Not certain from the listing — hosting, sandboxing of code execution, and secrets management are not detailed. Threats include host compromise if the model executes code locally or in an unsandboxed cloud environment.
Features 'Flow Awareness' with a shared timeline for collaboration and continuous improvement. Threats include blind spots in monitoring what code changes the agent proposes or applies, and evaluation gaming.
Not certain from the listing — no explicit security certifications (like SOC2), compliance alignments, or access control policies are mentioned.
Not certain from the listing — primarily focuses on human-AI collaboration rather than multi-agent marketplace interactions, though cascading failures could occur if integrated into broader CI/CD pipelines.
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.