DeepCoder-14B-Preview — agentic threat model

The model is a fine-tuned DeepSeek-R1-Distilled-Qwen-14B. Primary threats include adversarial prompt injection to bypass safety alignment, and the generation of subtly flawed or malicious code (reprogramming/mis-aligned outputs) that developers might trust blindly.

The model is fine-tuned on competitive programming and coding datasets. Threats include training data poisoning (introducing backdoors into generated code templates) and potential intellectual property/licensing contamination from the training corpus.

Not certain from the listing — The listing describes a raw foundation model deployable via Ollama rather than a fully realized agent framework. If integrated into Agentica or other frameworks, risks would include insecure tool execution of generated code.

Designed for easy deployment via Ollama. Threats include local host compromise if the Ollama API is exposed without authentication, and resource exhaustion (DoS) on the hosting infrastructure during heavy inference.

Not certain from the listing — No built-in guardrails, logging, or observability solutions are mentioned. Users deploying the model locally must implement their own monitoring and output validation to detect malicious code generation.

Not certain from the listing — As a free, open-source model, there are no built-in compliance controls, identity management, or access policies provided out-of-the-box.

Not certain from the listing — While developed in collaboration with Agentica, the model itself does not natively interact with an agent marketplace or multi-agent ecosystem unless integrated by the end-user.