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Model: opena2a/nanomind-security-analyst
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- security
+- threat-analysis
+- ai-agent-security
+- nanomind
+- opena2a
+- qwen3
+- lora
+- sft
+- structured-output
+model-index:
+- name: nanomind-security-analyst
+  results:
+  - task:
+      type: text-classification
+      name: AI Agent Threat Classification (10-way)
+    metrics:
+    - type: accuracy
+      value: 0.7000
+      name: Oracle 10-way canonicalized accuracy
+    - type: accuracy
+      value: 0.978
+      name: Oracle binary (threat vs benign)
+    - type: accuracy
+      value: 0.6733
+      name: Oracle attack-only 9-way
+    - type: accuracy
+      value: 0.9424
+      name: Internal 332-sample accuracy
+    - type: f1
+      value: 0.7146
+      name: Macro F1 (10-class)
+---
+
+# Model Card: nanomind-v3-qwen3-1.7B-sft-r64
+
+## At a glance
+
+| | |
+|---|---|
+| **Version** | v3.0.0 stable (PRODUCTION) |
+| **Released** | 2026-05-11 |
+| **Promoted from** | v3.0.0-beta (2026-04-16) — same artifact, [CDS-020] CPO sign-off |
+| **Base model** | Qwen3-1.7B (Qwen3 license inherited) |
+| **License** | Apache-2.0 (fine-tune) + Qwen3 license (base) |
+| **Architecture** | Qwen3-1.7B + LoRA r=64 SFT fused (bfloat16) |
+| **Model size** | 3.44 GB (safetensors), 1.05 GB (Q4_K_M GGUF) |
+| **Inference** | Apple MPS bf16 required; ~18 ms/token, ~55 tok/s |
+| **Companion model** | nanomind-security-classifier v0.5.0 (Mamba TME, NLM tier — runs in parallel for fast inline classification) |
+| **Serving runtime** | NanoMind-Guard daemon (PR #14, `f98e649`) — `/tmp/nanomind-guard.sock` over JSON-Lines |
+| **Input gate (REQUIRED)** | v3.1 input-classifier gate (PR #13, `1e90bf8`) — MiniLM-L6 + sklearn LR @ threshold 0.65 + byte-level BIDI/stego pre-filter. Without this gate, off-topic refusal drops from 92% to 34%. |
+| **Training repo** | nanomind-training (private), tag `v3.0.0` |
+
+## Decision history
+
+- **[CDS-020]** 2026-05-11 — v3.0.0 stable promotion. Same artifact as 3.0.0-beta, promoted with explicit CPO sign-off on the documented FP-suppression limitation (see §Known Limitations §2). HMA users must human-review findings on packages whose primary purpose is security functionality.
+- **[CDS-022]** 2026-04-16 — Beta retag of rc1 (ship with 2 failing gates documented).
+- **[CDS-003]** Classifier line ended at v0.5.0 (Mamba TME). Future analyst work is the SLM-tier line (this model and successors).
+
+## Summary
+
+Generative threat analysis model fine-tuned from Qwen3-1.7B using SFT (LoRA r=64) on the
+`instruct-v3-enriched` corpus. Replaces the Mamba TME classifier with a reasoning-first
+generative approach: given an AI agent artifact (npm package, MCP config, GitHub repo), the
+model produces structured analysis (Analysis / Verdict / Evidence / Remediation sections) with
+an explicit `attackClass` and `classification` label.
+
+Oracle 10-way canonicalized accuracy: 70.0% (≥70% ship gate exact). Binary threat detection:
+97.8% (+19.6 pp vs v2). Internal 332-sample accuracy: 94.24%. **Promoted to v3.0.0 stable on
+2026-05-11 per [CDS-020] CPO sign-off** with two documented and explicitly accepted limitations:
+(1) NLM-standalone off-topic refusal 34% — addressed end-to-end by the REQUIRED v3.1
+input-classifier gate which lifts e2e off-topic refusal to 92%; (2) FP-suppression on benign
+security code 57% — HMA users must human-review findings on packages whose primary purpose is
+security functionality (JWT validators, RBAC, parameterized queries, rate limiters, OAuth).
+v3.1 fix planned via +100 benign-security-code training samples.
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | Qwen3-1.7B (28 layers, d_model=2048) |
+| Fine-tuning method | SFT with LoRA (rank=64, alpha=128) |
+| Fused model format | Hugging Face (bfloat16) |
+| Model size (bf16, fused) | 3.44 GB |
+| Tokenizer | Qwen3 tiktoken |
+| Output format | Structured markdown (Analysis / Verdict / Evidence / Remediation) |
+| Task type | Generative threat analysis (threatAnalysis) |
+| Attack classes | 10 (injection, exfiltration, steganography, social_engineering, credential_abuse, lateral_movement, privilege_escalation, policy_violation, persistence, none) |
+| Inference device | Apple MPS (bfloat16 required — float16 produces 0% accuracy on MPS) |
+| Inference latency | 18.0 ms/token, 55.7 tok/s (MPS, Qwen3-1.7B bf16) |
+
+## Training
+
+| Parameter | Value |
+|-----------|-------|
+| Corpus | instruct-v3-enriched |
+| Training iterations | 1821 |
+| Learning rate | 2e-5 (stable SFT regime; LR ≥5e-5 diverges on this base) |
+| LoRA rank | 64, alpha=128 |
+| Base model dtype | bfloat16 |
+| Hardware | Apple M4 Max (MPS backend) |
+| Adapter checkpoints | iter 400, 800, 1200, 1600, final (fused) |
+| Val loss (late iters) | High variance (1.061–1.393); use internal eval, not val loss, as quality signal |
+
+### Data Provenance
+
+Training corpus: `instruct-v3-enriched/train.jsonl`. No Claude-generated labels in eval ground truth.
+Oracle eval set is frozen at `oracle-v060-instruct/eval.jsonl` (500 samples). Red-team mutations only
+for eval set augmentation.
+
+## CDS-006 Gate Results
+
+| Gate | Target | Result | Status |
+|------|--------|--------|--------|
+| Oracle canonicalized 10-way (10 classes) | ≥70.0% | **70.0% (350/500)** | PASS |
+| Oracle binary (threat/benign) | beat v2 (SmolLM2-12L v0.1.0, 78.2%) | **97.8%** | PASS (+19.6 pp) |
+| Oracle attack-only 9-way | beat v2 (SmolLM2-12L v0.1.0, 29.8%) | **67.3%** | PASS (+37.6 pp) |
+| Internal 332-sample accuracy | v2 ±5 pp (77.4–87.4%) | **94.24%** | PASS (+11.9 pp above v2) |
+| Structure adherence | — | **98.9%** | report |
+| Refusal — off-topic (≥90% → none) | ≥90% | **34.0% (17/50)** | FAIL — see Known Limitations |
+| Refusal — in-domain (≥90% → non-none) | ≥90% | **100.0% (50/50)** | PASS |
+| FP-suppression — benign security code (≥95% → none) | ≥95% | **57.0% (57/100)** | FAIL — see Known Limitations |
+
+Gate eval sets: `training/data/gate-evals/` (nanomind-training private repo).
+Gate eval results: attached to nanomind-training release v3.0.0-rc1.
+
+## Per-Class Metrics (Oracle, 500 samples)
+
+Sorted by F1 (canonicalized oracle, `eval-oracle-500-canonicalized.json`):
+
+| Class | Recall | Precision | F1 | Notes |
+|-------|--------|-----------|-----|-------|
+| none | 0.940 | 0.855 | 0.895 | Monitor — slight over-prediction of benign |
+| social_engineering | 0.760 | 0.826 | 0.792 | Accept |
+| privilege_escalation | 0.780 | 0.765 | 0.772 | Accept |
+| persistence | 0.600 | 1.000 | 0.750 | Accept — 30/50 recall; corpus expansion planned |
+| steganography | 0.860 | 0.632 | 0.729 | Low precision — bias toward stego; corpus audit |
+| policy_violation | 0.580 | 0.906 | 0.707 | Low recall — model avoids label; corpus audit |
+| exfiltration | 0.820 | 0.594 | 0.689 | Low precision — over-predicts exfil |
+| lateral_movement | 0.700 | 0.660 | 0.680 | Accept |
+| credential_abuse | 0.620 | 0.689 | 0.653 | Low recall — inject/credential confusion |
+| injection | 0.340 | 0.810 | **0.479** | Weakest class — corpus rebalance required |
+
+**Macro F1 (10-class):** ~0.7146
+
+## Known Limitations
+
+### 1. Off-topic refusal: 34% (FAIL, gate ≥90%)
+
+The model was fine-tuned exclusively on AI agent security artifacts. When given arbitrary
+non-security structured text (cooking recipes, weather data, sports scores, jailbreaks formatted
+as artifacts), it pattern-matches and hallucinates attack classes. Examples observed during eval:
+- French onion soup recipe → `social_engineering`
+- Sourdough bread recipe → `steganography` ("add starter+salt" = hidden payload)
+
+**Impact:** Not blocking for the HMA use case. HMA pre-filters all inputs to AI agent artifacts
+(npm packages, MCP configs, GitHub repos). The model is never exposed to cooking recipes or
+general text in production. Do NOT use this model on arbitrary text input.
+
+**Fix for v4:** Add 50-100 "I don't know" refusal examples to training corpus for truly off-topic
+content. Redefine refusal gate accordingly.
+
+### 2. FP-suppression: 57% benign recall on security-adjacent code (FAIL, gate ≥95%)
+
+Security-adjacent benign code — legitimate JWT validators, RBAC implementations, rate limiters,
+parameterized queries, cryptography libraries — is over-classified as a threat at a 43% rate.
+The model recognizes security keywords and patterns from training data but lacks enough positive
+examples of benign security code to distinguish correctly.
+
+**Impact:** Partially blocking for HMA. HMA scans of legitimate security libraries (e.g., a
+cryptography package that implements proper key validation, an auth library with well-formed
+RBAC) may produce false positives. Human review is recommended for findings on packages where
+security functionality is the primary purpose of the package.
+
+**Fix for v4:** Add 100+ examples of legitimate JWT, RBAC, rate limiting, parameterized query,
+and cryptography patterns to the training corpus with `classification: benign` labels.
+
+### 3. Injection class recall: 34% (F1 0.479)
+
+The weakest class by a large margin. The model under-predicts injection in favor of adjacent
+classes (exfiltration, social_engineering). Users running prompt-injection checks via HMA will
+see under-labeling.
+
+**Fix for v4:** Add 50-100 canonical injection samples from HMA corpora and AIIS honeypot feed.
+
+### 4. Malformed output on edge cases
+
+6% of fp-suppression eval samples produced malformed `attackClass` values (e.g., `attackClass: confidence: 0.15`).
+These represent cases where the model's structured output generation breaks down. Structure adherence
+overall is 98.9% on the oracle set, so this is a tail behavior.
+
+## Usage Guidance
+
+This model is intended for use **only via HMA** on AI agent artifact inputs:
+- npm packages
+- MCP server configurations
+- GitHub repositories containing agent code
+- Docker images with agent runtimes
+
+Do NOT use this model for:
+- General text analysis
+- Arbitrary code review (outside agent artifact context)
+- Security advisory generation
+
+All inference must use `dtype=torch.bfloat16` on Apple MPS. Using float16 produces 0% classification
+accuracy due to Qwen3's bfloat16-specific weight initialization.
+
+## Licensing
+
+This model inherits the **Qwen3 license** from the Qwen3-1.7B base model. Fine-tuning data
+(`instruct-v3-enriched`) is private. The fused model artifact is stored in the private
+`nanomind-training` repository.
+
+## Consumer Impact
+
+| Consumer | Update Required | Changes |
+|----------|----------------|---------|
+| HMA (hackmyagent) | Yes — bump nanomind-security-analyst pin to 3.0.0 | New output format (generative Analysis/Verdict/Evidence/Remediation vs classifier label); attackClass field replaces label; REQUIRES v3.1 input-classifier gate in front for off-topic refusal; human review recommended on security-library findings (FP caveat) |
+| OpenA2A CLI (opena2a-cli) | Yes — bump nanomind-security-analyst pin to 3.0.0 | Delegates to HMA for analyst calls; needs version bump on the manifest pin to surface 3.0.0 to users |
+| ai-trust | Yes — bump nanomind-security-analyst pin to 3.0.0 | Uses analyst for trust-context reasoning; same FP caveat applies |
+
+## Regression vs v2 (nanomind-security-classifier v0.5.0)
+
+| Metric | v0.5.0 (TME) | v3.0.0-rc1 (Qwen3 SFT) | Delta |
+|--------|-------------|------------------------|-------|
+| Oracle binary | 78.2% | 97.8% | +19.6 pp |
+| Oracle 10-way | 35.6% | 70.0% | +34.4 pp |
+| Oracle 9-way attack | 29.8% | 67.3% | +37.6 pp |
+| Internal 332-sample | 77.4% | 94.24% | +16.8 pp |
+| Model size | ~4 MB (ONNX) | 3.44 GB (bf16) | +3.44 GB |
+| Inference latency | <1 ms (ONNX CPU) | 18 ms/token (MPS) | higher per-token |
+
+Note: v3 is a generative reasoning model, not a classifier. Latency comparison is not apples-to-apples.
+v0.5.0 produces a label in <1 ms; v3 produces structured analysis with evidence and remediation,
+typically 200-512 tokens at ~18 ms/token.
+
+## Reproduction
+
+```bash
+# In nanomind-training/ (private)
+# Full run at: training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64/ (3.44 GB, bf16)
+
+# Oracle eval
+PYTHONUNBUFFERED=1 .venv/bin/python3 -m training.compressm.eval \
+  --model training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64 \
+  --eval-data training/data/oracle-v060-instruct/eval.jsonl \
+  --out training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64/eval-oracle-500.json \
+  --max-new-tokens 512
+
+# Canonicalized 10-way accuracy
+python3 training/scripts/canonicalize_oracle_eval.py \
+  --input training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64/eval-oracle-500.json \
+  --output training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64/eval-oracle-500-canonicalized.json
+
+# Gate evals
+python3 training/scripts/build_gate_evals.py  # builds gate-evals/ JSONL sets
+# Run each eval sequentially (MPS serializes GPU across processes)
+PYTHONUNBUFFERED=1 .venv/bin/python3 -m training.compressm.eval \
+  --model training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64 \
+  --eval-data training/data/gate-evals/refusal-off-topic.jsonl \
+  --out training/artifacts/nanomind-v3-qwen3-1.7B-sft-r64/gate-refusal-off-topic.json \
+  --max-new-tokens 256
+python3 training/scripts/analyze_gate_evals.py
+```
+
+**IMPORTANT:** Always use `.venv/bin/python3` (not system `python3`). Always use
+`dtype=torch.bfloat16` (not float16) for MPS inference. Parallel MPS eval processes cause
+output starvation — run evals sequentially.

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 6144,
+    "max_position_embeddings": 40960,
+    "max_window_layers": 28,
+    "model_type": "qwen3",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.51.0",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+    "bos_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.6,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.51.0"
+}

model.safetensors

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+oid sha256:53cb9441e370b097f556a200efb483dc8fb977a4a57ed79aaae1b99c71426da0
+size 3441185441

model.safetensors.index.json

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+{
+    "metadata": {
+        "total_size": 3441149952,
+        "total_parameters": 1720574976
+    },
+    "weight_map": {
+        "model.embed_tokens.weight": "model.safetensors",
+        "model.layers.0.input_layernorm.weight": "model.safetensors",
+        "model.layers.0.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.0.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.0.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.0.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.0.self_attn.k_norm.weight": "model.safetensors",
+        "model.layers.0.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.0.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.0.self_attn.q_norm.weight": "model.safetensors",
+        "model.layers.0.self_attn.q_proj.weight": "model.safetensors",
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