Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/davidmatousek/tachinpx agentmods add rules/davidmatousek/tachi/model-theftWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/davidmatousek/tachi/model-theft)<a href="https://agentmods.dev/rules/davidmatousek/tachi/model-theft"><img src="https://agentmods.dev/badge/rules/davidmatousek/tachi/model-theft.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.02270 |
| Opus 5 | $0.00022 | $0.01135 |
| Sonnet 5 | $0.00009 | $0.00454 |
| Haiku 4.5 | $0.00004 | $0.00227 |
Grade A, and why
model-theft scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metadata
category: llm
threat_class: LLM
dfd_targets: [Data Store, Process]
owasp_references: [OWASP LLM06:2026, OWASP LLM04:2026]
output_schema: ../../../schemas/finding.yaml
Model Theft Threat Agent
Purpose
Detects threats where an attacker attempts to steal, replicate, or extract proprietary model assets. Model theft encompasses direct exfiltration of model weights and parameters, API-based model extraction where systematic querying reconstructs a functional copy, and unauthorized access to model artifacts stored in training infrastructure. Successful model theft results in loss of intellectual property, enables adversaries to discover model vulnerabilities offline, and eliminates competitive advantages derived from proprietary model capabilities. This agent identifies weight exfiltration, API-based extraction, model artifact exposure, and side-channel attacks that reveal model architecture or parameters.
Detection Scope
Trigger Keywords
This agent activates when a DFD element name or description matches any of the following patterns (case-insensitive):
LLMmodelGPTClaudeweightscheckpointinferencemodel registrymodel servingmodel APIfine-tuned
Applicable DFD Element Types
- Data Store: Model registries, weight storage systems, checkpoint repositories, artifact stores, and any storage containing model parameters, configurations, or training outputs.
- Process: Model serving endpoints, inference APIs, training pipelines, and fine-tuning processes that have access to model weights or produce outputs from which model behavior can be inferred.
Detection Patterns
- Direct Weight Exfiltration: Unauthorized access to stored model files, parameters, or checkpoints. Look for:
- Model weight files stored in shared storage without access controls (S3 buckets, NFS mounts, model registries)
- Overly broad IAM permissions on model artifact storage
- Training pipelines that write checkpoints to world-readable locations
- Model serving infrastructure where the container filesystem exposes weight files
- Absence of encryption at rest for model artifacts
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 185 lines · 43 tokens per session scan A a207b7d00b66
model-theft is a cursor rule published in the GitHub repository davidmatousek/tachi (90 stars, last pushed 25d ago), licensed Apache-2.0. It adds 43 tokens to every session and 2,270 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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