Text Classification
Transformers
Safetensors
deberta-v2
agents
agent-safety
tool-use
prompt-injection
deberta-v3
text-embeddings-inference
Instructions to use kontext-security/Merlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kontext-security/Merlin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kontext-security/Merlin")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kontext-security/Merlin") model = AutoModelForSequenceClassification.from_pretrained("kontext-security/Merlin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,980 Bytes
21b6f2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | ---
license: other
license_name: merlin-research-release
base_model: microsoft/deberta-v3-xsmall
library_name: transformers
pipeline_tag: text-classification
tags:
- agents
- agent-safety
- tool-use
- prompt-injection
- deberta-v3
---
# Merlin
Merlin is a 70.8M-parameter local encoder that classifies a proposed AI-agent
tool invocation as `safe` or `unsafe` using its surrounding context.
This repository contains the portable checkpoint. Use the reference package at
<https://github.com/kontext-security/merlin>; a generic Transformers pipeline
does **not** reproduce the benchmark because Merlin uses four independently
budgeted fields, deterministic history normalization, and a validation-fitted
calibrator.
## Inputs
1. user request
2. prior interaction history
3. current tool name and arguments
4. tool descriptions/schemas
Prior ReAct history is converted to canonical JSON tool/argument/observation
events. `Thought` and `Final Answer` text is excluded. The current action is
reduced to tool name and arguments. Each field has its own token budget within a
512-token packed sequence.
## Results
Strict binary evaluation on TS-Bench (`0.0` safe; `0.5` and `1.0` unsafe), fixed
0.5 threshold:
| Split | N | Accuracy | Precision | Recall | F1 |
|---|---:|---:|---:|---:|---:|
| All TS-Bench | 7,182 | 91.19% | 92.66% | 88.45% | 90.51% |
| ASB-Traj | 5,231 | 99.73% | 99.76% | 99.68% | 99.72% |
| AgentDojo-Traj | 1,220 | 71.80% | 51.14% | 50.85% | 51.00% |
| AgentHarm-Traj | 731 | 62.38% | 83.42% | 59.43% | 69.41% |
The pooled score is dominated by ASB-Traj and is not evidence of uniform
cross-environment performance. See the GitHub repository for the full protocol,
preprocessing code, data provenance, system measurements, and limitations.
## Training
- Base: `microsoft/deberta-v3-xsmall` at revision
`4b419818330868dff6a60ad3e6b1c730f8b8c0c6`
- Train: 2,192 examples (841 safe, 1,351 unsafe)
- Validation: 789 examples
- Epochs: 5; learning rate: 2e-5; batch size: 8; gradient accumulation: 2
- Sigmoid calibration coefficient: 1.427213430140093
- Sigmoid calibration intercept: 2.953687013257505
- Default threshold: 0.5
## Intended use
Use Merlin as a low-latency, local safety signal before executing an agent tool
call. It is not a complete authorization layer and should be combined with
least privilege, deterministic policy, sandboxing, and human confirmation for
consequential actions.
Do not use it as the sole control for high-impact actions, as a general content
moderator, or outside the documented input representation without evaluation.
## Data and license
The base model is MIT licensed. Merlin was fine-tuned on TS-Bench data from
<https://github.com/MurrayTom/ToolSafe>. That repository had no explicit
repository license at release time, so the checkpoint is marked `other` and no
raw TS-Bench examples are mirrored here. Review upstream terms before commercial
use or redistribution. Reference code is Apache-2.0.
|