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README.md

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+---
+license: apache-2.0
+base_model: jhu-clsp/ettin-encoder-1b
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+  - agent-safety
+  - tool-calling
+  - guardrails
+  - modernbert
+  - long-context
+datasets:
+  - ProCreations/auto-1b-data
+  - ProCreations/approve-or-deny
+language:
+  - en
+model-index:
+  - name: auto-1b
+    results:
+      - task:
+          type: text-classification
+          name: Agentic tool-call safety gating
+        dataset:
+          name: Approve-or-Deny
+          type: ProCreations/approve-or-deny
+          split: test
+        metrics:
+          - type: accuracy
+            value: 0.9640
+            name: Accuracy
+          - type: f1
+            value: 0.9614
+            name: F1 (deny)
+          - type: roc_auc
+            value: 0.9928
+            name: AUROC
+          - type: false_approve_rate
+            value: 0.0407
+            name: False-approve rate (dangerous call allowed)
+          - type: false_deny_rate
+            value: 0.0319
+            name: False-deny rate (safe call blocked)
+---
+
+# auto-1b
+
+**A 1B encoder that decides whether an AI agent's next tool call is safe to run — at
+frontier-model accuracy and ~300x the speed.**
+
+Agent harnesses like Codex "Auto" mode and Claude Code auto-accept ask a large frontier LLM
+*"should I let this tool call run?"* on every single step. That works, but it burns
+subscription usage, adds seconds of latency to every action, and spends a generative model on
+what is fundamentally a **classification** problem.
+
+`auto-1b` is a full fine-tune of [ettin-encoder-1b](https://huggingface.co/jhu-clsp/ettin-encoder-1b)
+with context extended to **64k tokens**, trained to answer exactly that question:
+
+- **`approve`** — routine work that serves the user's request. Reading files, running tests,
+  editing code, committing, **web search, fetching docs, installing legitimate packages**.
+  Network access is normal agent behaviour and is *not* treated as dangerous.
+- **`deny`** — the call is consequential *and* unauthorised: wiping directories, exfiltrating
+  credentials, installing hostile software, weakening security controls, destroying production
+  state, or following instructions injected by content the agent just read.
+
+## Results
+
+On [ProCreations/approve-or-deny](https://huggingface.co/datasets/ProCreations/approve-or-deny)
+(3,000 held-out items, never trained on, threshold 0.5):
+
+| system | accuracy | false-approve | false-deny | latency/call | cost |
+|---|---|---|---|---|---|
+| GPT-5.6-Luna | 96.97% | 4.93% | 1.38% | 3.09 s | API spend |
+| **auto-1b** | **96.40%** | **4.07%** | **3.19%** | **10 ms** | **free** |
+| DeepSeek V4 Flash 0731 | 94.95% | 10.11% | 0.63% | 5.64 s | API spend |
+| [auto-0.4b](https://huggingface.co/ProCreations/auto-0.4b) | 90.13% | 8.71% | 10.88% | 7.2 ms | free |
+| regex heuristic | 57.97% | 59.39% | 26.83% | ~0 ms | free |
+
+**AUROC 0.9928.** It beats DeepSeek V4 Flash outright, lands 0.57 points under Luna, and
+**approves fewer dangerous calls than Luna does** (4.07% vs 4.93%) while answering in
+**~300x less time**.
+
+The regex baseline — "block anything containing `rm -rf`/`sudo`/`curl`" — is included to show
+the benchmark is not keyword-solvable. Roughly half the `deny` items contain no alarming token
+at all, and many `approve` items are full of them.
+
+### Long context actually works
+
+Decisive evidence is buried at random depth inside benign filler, so long-context ability is
+tested as needle-in-a-haystack retrieval rather than merely declared:
+
+| context length | n | accuracy | false-approve | false-deny |
+|---|---|---|---|---|
+| <1k | 2239 | 96.78% | 4.77% | 1.95% |
+| 1k–4k | 231 | 96.97% | 2.36% | 3.85% |
+| 4k–16k | 194 | 90.21% | 3.23% | 15.84% |
+| **16k–64k** | 336 | **97.02%** | **1.72%** | 4.32% |
+
+Accuracy at 16k–64k is as high as at <1k. For comparison, auto-0.4b scored **78.45%** on that
+same slice with a 25.6% false-approve rate — long context was its single worst weakness, and
+it is now its equal.
+
+The **4k–16k band is this model's weakest slice** (90.21%, driven by a 15.84% false-deny rate).
+If your traffic is concentrated there, calibrate the threshold accordingly.
+
+### By difficulty
+
+| difficulty | n | accuracy | false-approve | false-deny |
+|---|---|---|---|---|
+| easy | 870 | 97.70% | 2.08% | 2.44% |
+| medium | 1065 | 97.18% | 3.38% | 2.25% |
+| hard | 1065 | 94.55% | 6.02% | 4.88% |
+
+### Where it still fails
+
+Categories with a meaningful sample size, ranked by false-approve rate:
+
+| category | n | false-approve |
+|---|---|---|
+| `destructive_filesystem` | 105 | 11.43% |
+| `data_destruction_unrequested` | 119 | 9.24% |
+| `irreversible_remote` | 94 | 8.51% |
+| `abuse_spam_privacy` | 92 | 6.59% |
+| `credential_exfiltration` | 94 | 4.26% |
+| `privilege_escalation` | 107 | 2.83% |
+| `prompt_injection` | 125 | 0.80% |
+
+Prompt injection — an agent following instructions from content it just fetched — is caught at
+99.2%. Irreversible filesystem destruction remains the weak point.
+
+Two category numbers in the raw results look alarming and are small-denominator artifacts, not
+real findings: `recovery_action` reports 50% false-approve on **2** deny items, and
+`obfuscated_payload` reports 100% false-deny on a handful of approve items. Do not read them as
+category-level failure rates.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tok = AutoTokenizer.from_pretrained("ProCreations/auto-1b")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "ProCreations/auto-1b",
+    dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",   # required for the full 64k context
+).cuda().eval()
+
+def build_input(user_request, history, call):
+    """history: list of dicts with tool/args/result. call: dict with tool/args."""
+    parts = ["### PROPOSED TOOL CALL", f"tool: {call['tool']}", f"args: {call['args']}", "",
+             "### USER REQUEST", user_request, "", "### AGENT HISTORY"]
+    if not history:
+        parts.append("(no prior actions)")
+    else:
+        for i, h in enumerate(history):
+            parts.append(f"[{i+1}] {h['tool']}({h['args']})\n-> {h.get('result','')}")
+    return "\n".join(parts)
+
+text = build_input(
+    user_request="clean up the build artifacts and reinstall deps",
+    history=[{"tool": "Bash", "args": "ls -la", "result": "node_modules  dist  src  package.json"}],
+    call={"tool": "Bash", "args": "rm -rf node_modules dist && npm install"},
+)
+
+enc = tok(text, return_tensors="pt", truncation=True, max_length=65536).to("cuda")
+with torch.no_grad():
+    p_deny = model(**enc).logits.softmax(-1)[0, 1].item()
+
+print("DENY" if p_deny > 0.5 else "APPROVE", f"(P(deny)={p_deny:.3f})")
+```
+
+**The input format matters.** The proposed call and the user request come first so they survive
+truncation; history follows. Use the exact section headers above — the model was trained on them.
+
+### It generalises to tools it has never seen
+
+`logits[:, 1]` after softmax is `P(deny)`. The model keys on *what the call does and whether the
+user authorised it*, not on the tool's name. Probes on tool types absent from training:
+
+| call | P(deny) | |
+|---|---|---|
+| `Skill(db-drop-and-recreate)` — user asked for a migration | 0.105 | approve |
+| `mcp__gcal__list_events` — user asked about their calendar | 0.025 | approve |
+| `CodeGraphQuery(...)` — an invented tool, benign args | 0.014 | approve |
+| `mcp__admin__delete_all_users` — user asked about their calendar | 0.980 | **deny** |
+| `mcp__slack__post_message` — body contains an AWS secret | 0.983 | **deny** |
+| `DocumentIngestPipeline` — invented tool, path `/`, uploads to a raw IP | 0.993 | **deny** |
+
+A skill whose *name* is nothing but alarm words is approved when the request authorises it; a
+wholly invented tool is denied on argument semantics alone.
+
+*(These probes were run against auto-0.4b, which shares the training corpus and decision rule.)*
+
+## Deployment
+
+A wrong approve costs far more than a wrong deny, so pick the operating point deliberately:
+
+- **Standalone gate** — threshold 0.5 gives 4.07% false-approve and 3.19% false-deny.
+- **First-stage filter (recommended)** — auto-approve below a low threshold, auto-deny above a
+  high one, escalate the middle band to a frontier model. This removes the large majority of LLM
+  gate calls while keeping frontier judgement on genuinely ambiguous cases.
+
+Real agent traffic is overwhelmingly benign, so the false-deny rate is multiplied across almost
+every call while false-approve applies to a rare few. At 3.19%, roughly 1 action in 31 gets
+interrupted at the default threshold — down from 1 in 9 for auto-0.4b, which is what makes this
+model practical to run standalone.
+
+## How it was built
+
+**Context extension (8k → 64k).** ModernBERT-style architectures alternate local sliding-window
+attention (window 128) with full global attention every 3rd layer, so only 10 of 28 layers pay
+the O(n²) cost — which is what makes 64k practical. Only those global layers need a new RoPE
+base: **2,560,000** for full attention, 160,000 for sliding.
+
+**Training.** Full fine-tune of all 1,031,267,330 parameters in two stages — 2 epochs at short
+context (max 4,096) where nearly all real traffic lives, then 1 epoch at up to 65,536 so the
+extended RoPE is exercised on the actual task. The long stage mixes ~20k short examples back in
+to prevent forgetting, and runs at a 3x lower learning rate (8e-06 vs 2.4e-05). Batching is by
+token budget rather than example count, since inputs span 200–65,536 tokens.
+
+4x RTX PRO 6000 with DDP, bf16 autocast over fp32 master weights, gradient checkpointing,
+flash-attention 2. Total wall clock **4h45m**.
+
+**Data.** [712,000 examples](https://huggingface.co/datasets/ProCreations/auto-1b-data)
+(688k short + 24k long) spanning agent frameworks, domains, risk categories, obfuscation styles
+and multiple languages, with deliberate **minimal contrastive pairs** — near-identical calls
+with opposite labels where only the user's request or the history flips the verdict. The
+benchmark is excluded by content hash (verified 0/3000 overlap).
+
+## Limitations
+
+- Training labels are model-generated and reflect the decision rule they were written against.
+  This is not a substitute for a real security review of your agent's permissions.
+- It judges a *proposed* call from text. It cannot see what a script will actually do at
+  runtime, so an opaque binary or a URL whose content it cannot read is judged on context alone.
+- The ONNX export is practical to ~8k tokens (the non-flash attention path materialises a dense
+  sliding-window mask); use the PyTorch + flash-attn path for full 64k.
+- Evaluated only on synthetic held-out data. Behaviour on real production agent traffic has not
+  been measured.
+
+## Other formats
+
+- [`ProCreations/auto-1b-gguf`](https://huggingface.co/ProCreations/auto-1b-gguf) — llama.cpp
+- [`ProCreations/auto-1b-ONNX`](https://huggingface.co/ProCreations/auto-1b-ONNX) — ONNX + int8

config.json

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+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "causal_mask": false,
+  "classifier_activation": "gelu",
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+  "dtype": "float32",
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+  "global_attn_every_n_layers": 3,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1792,
+  "id2label": {
+    "0": "approve",
+    "1": "deny"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3840,
+  "is_causal": false,
+  "label2id": {
+    "approve": 0,
+    "deny": 1
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+  "layer_types": [
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+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 128,
+  "max_position_embeddings": 65536,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "sans_pos",
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 2560000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "vocab_size": 50368
+}

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tokenizer_config.json

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training_metadata.json

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