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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "02ef1ad7",
   "source": [
    "# App Creation β€” PDF Injection Detector (MiMo-7B)\n",
    "\n",
    "The build log for the Hugging Face Space at\n",
    "[`BentoUniAcc/Mimo_Injection_detector`](https://huggingface.co/spaces/BentoUniAcc/Mimo_Injection_detector):\n",
    "what the app does, which decisions were forced rather than chosen, what broke on the way, and the\n",
    "checks that say it is still faithful to the notebooks it quotes.\n",
    "\n",
    "**The order of this notebook is the order the work happened in.** Part 2 fits the family-naming\n",
    "model, and it comes first because the app was designed around what that model can do. The\n",
    "evaluation stage had already established the shape of the problem: MiMo-7B answers *\"is there a\n",

    "payload in this text?\"* well (F1 0.945) and *\"which family of attack is it?\"* badly (43%). Since\n",
    "the whole point of the interface is to tell someone **what to do about the file**, and the advice is\n",
    "selected by family, an app built on MiMo's family guess would have been an app that hands out the\n",
    "wrong instructions more often than the right ones.\n",
    "\n",
    "So the family classifier was built first, from signals that were already available, and the app was\n",
    "written to consume it: `app.py` asks MiMo for the verdict and evidence, and asks the model in\n",
    "`family_naming_model.pkl` for the name.\n",
    "\n",
    "The remaining parts document the app itself. Every cell in them runs against the **deployed** files,\n",
    "so if one fails, the Space is wrong and not the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 β€” What it is\n",
    "\n",
    "Upload a PDF. It is rendered to text with the extractor that built the project corpus, the regions\n",
    "carrying structural markers are ranked, the whole document is cut into **batches that fit one run\n",
    "of the model**, and **MiMo-7B-RL** reads the batch you choose β€” reporting whether a payload is\n",
    "hidden there and quoting the substring that convinced it.\n",
    "\n",
    "| File | Role |\n",
    "|---|---|\n",
    "| `app.py` | Gradio UI, batching, report aggregation. No detection logic. |\n",
    "| `corpus_text.py` | PDF bytes to skeleton to candidate regions. |\n",
    "| `mimo.py` | Prompt, prefill, parser, and both model runtimes. |\n",
    "| `neighbours.py` | Part A embedding index and nearest-neighbour lookup. |\n",
    "| `test_fidelity.py` | Proves the extraction reproduces the published corpus exactly. |\n"
   ],
   "id": "b7ff9ba8"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "51f5cac4",
   "source": [
    "## 2 β€” The family-naming model\n",
    "\n",
    "### 2.0 β€” What it has to do, and why it is not a language model\n",
    "\n",
    "Thirteen answers are possible for any uploaded PDF: twelve known injection families, or `none`. The\n",
    "app needs a **ranked short-list with confidences** rather than a single assertion, because the thing\n",
    "attached to the answer is remediation advice, and a user who is shown one confident wrong family\n",
    "acts on it.\n",
    "\n",
    "Four signals about the same document were already available from earlier stages of the project, and\n",
    "none of them is good enough alone:\n",
    "\n",
    "| Signal | Width | Where it comes from |\n",
    "|---|---|---|\n",
    "| structural signatures | 12 | the regex detector in `corpus_text.py` β€” which of twelve known payload shapes appear |\n",
    "| MiMo's own opinion | 15 | its family guess one-hot, its verdict, and whether its answer parsed |\n",
    "| document shape | 20 | 17 measurements of the carrier PDF from the EDA, plus 3 log companions |\n",
    "| neighbour vote | 13 | weighted vote of the 20 nearest corpus documents in the embedding index |\n",
    "\n",
    "Fusing several weak-but-different sources is the whole idea: each one is wrong in its own way, and a\n",
    "model over all four can learn when to trust which. What ships is a\n",
    "`HistGradientBoostingClassifier` β€” a few hundred small decision trees, each correcting the errors of\n",
    "the ones before it. It is under a megabyte, runs on the CPU in well under a second, and contains no\n",
    "neural network at all.\n",
    "\n",
    "### 2.1 β€” The data\n",
    "\n",
    "One row per document, 1,100 of them, assembled from the three dataset repos and joined on\n",
    "`file_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "30b5e3c0",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "corpus (1100, 44) | embeddings (1100, 768) | MiMo answers (1100, 11)\n",
      "13 classes, clean share 18.2%"
     ]
    }
   ],
   "source": [
    "%pip install -q scikit-learn joblib pymupdf\n",
    "\n",
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import corpus_text          # the Space's own extractor - imported, never re-implemented\n",
    "\n",
    "SEED = 42\n",
    "np.random.seed(SEED)\n",
    "\n",
    "MODEL_FILE = 'family_naming_model.pkl'      # what the app loads, written at the end of this part\n",
    "\n",
    "HF = 'https://huggingface.co/datasets/Cyber-security-final-project/'\n",
    "CORPUS = HF + ('HARMLESS_Synthetic_Injected_PDFs_EDA/resolve/main/Datasets/'\n",
    "               'synthetic_corpus_part2_clustered.parquet')\n",
    "EMB    = HF + ('Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition/resolve/main/'\n",
    "               'Part_A_Outputs/corpus_embeddings.parquet')\n",
    "ANS    = HF + ('Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition/resolve/main/'\n",
    "               'Part_B_Outputs/part_b_answers.json')\n",
    "\n",
    "corpus  = pd.read_parquet(CORPUS)\n",
    "emb_df  = pd.read_parquet(EMB)\n",
    "answers = pd.read_json(ANS)\n",
    "\n",
    "FAM_LIST  = sorted(corpus_text.INJECTION_MARKERS)\n",
    "CLASSES   = FAM_LIST + ['none']\n",
    "cls_index = {c: i for i, c in enumerate(CLASSES)}\n",
    "\n",
    "# Every table is re-indexed onto the corpus's own row order. Joining on file_id rather than\n",
    "# trusting three files to have been written in the same order is cheap insurance against the\n",
    "# quietest possible bug: a model trained on correctly-shaped features and shuffled labels.\n",
    "emb_df = emb_df.set_index('file_id').loc[corpus['file_id']].reset_index()\n",
    "E = emb_df.drop(columns='file_id').to_numpy(np.float32)\n",
    "assert np.allclose(np.linalg.norm(E, axis=1), 1, atol=1e-3), 'index is not unit-normalised'\n",
    "\n",
    "mimo_ans = (answers[answers['model'] == 'mimo']\n",
    "            .set_index('file_id').loc[corpus['file_id']].reset_index())\n",
    "\n",
    "y_idx = np.array([cls_index[v] for v in corpus['injection_type']])\n",
    "print(f'corpus {corpus.shape} | embeddings {E.shape} | MiMo answers {mimo_ans.shape}')\n",
    "print(f'{len(CLASSES)} classes, clean share {(corpus[\"injection_type\"] == \"none\").mean():.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "ad06b14c",
   "source": [
    "### 2.2 β€” Building the four signal blocks\n",
    "\n",
    "Each block is built here the way `family_model.py` will have to rebuild it from an uploaded file,\n",
    "which is the constraint that shapes all of them. The signatures are recomputed with\n",
    "`corpus_text.detect_markers` β€” the app's own detector, imported rather than reimplemented β€” instead\n",
    "of read from the corpus's stored column, and the document-shape columns come from\n",
    "`doc_features.NUMERIC` for the same reason. **If the training feature and the serving feature ever\n",
    "stop being the same function, the model scores something the app cannot produce**, and nothing\n",
    "raises an error when that happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "647af01c",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "markers   (1100, 12)   (82.1% of files carry at least one)\n",
      "MiMo      (1100, 13) + (1100, 2)\n",
      "doc shape (1100, 20)\n",
      "blocks    {'markers': (0, 12), 'mimo': (12, 27)}"
     ]
    }
   ],
   "source": [
    "import doc_features\n",
    "\n",
    "# --- 1. structural markers, recomputed with the DEPLOYED detector -----------------------\n",
    "M = np.zeros((len(corpus), len(FAM_LIST)), np.float32)\n",
    "for r, s in enumerate(corpus['skeleton']):\n",
    "    for h in corpus_text.detect_markers(s):\n",
    "        M[r, FAM_LIST.index(h)] = 1.0\n",
    "\n",
    "# --- 2. MiMo's opinion, as a feature rather than as the answer --------------------------\n",
    "mimo_fam = np.zeros((len(corpus), len(CLASSES)), np.float32)\n",
    "for r, f in enumerate(mimo_ans['pred_family']):\n",
    "    mimo_fam[r, cls_index.get(f, cls_index['none'])] = 1.0\n",
    "mimo_extra = np.c_[mimo_ans['pred_injected'].to_numpy(np.float32),\n",
    "                   mimo_ans['parse_ok'].to_numpy(np.float32)]\n",
    "\n",
    "# --- 3. document shape, in the order doc_features.py will rebuild at inference -----------\n",
    "NUM = doc_features.NUMERIC\n",
    "N = np.nan_to_num(corpus[NUM].astype(np.float32).to_numpy())\n",
    "N = np.c_[N, np.log1p(np.abs(corpus[doc_features.LOGGED].astype(np.float32).to_numpy()))]\n",
    "\n",
    "BLOCKS = {'markers': (0, M.shape[1]),\n",
    "          'mimo':    (M.shape[1], M.shape[1] + mimo_fam.shape[1] + mimo_extra.shape[1])}\n",
    "\n",
    "print(f'markers   {M.shape}   ({(M.sum(1) > 0).mean():.1%} of files carry at least one)')\n",
    "print(f'MiMo      {mimo_fam.shape} + {mimo_extra.shape}')\n",
    "print(f'doc shape {N.shape}')\n",
    "print('blocks   ', BLOCKS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "f4f66e8a",
   "source": [
    "### 2.3 β€” The neighbour vote, and the self-match that would have faked it\n",
    "\n",
    "The fourth block asks *what does this document resemble?* Each file's embedding is compared against\n",
    "the others, the 20 nearest are taken, and each votes for its own family with a weight equal to its\n",
    "similarity.\n",
    "\n",
    "The masked diagonal matters more than it looks. Inside cross-validation, a document allowed to find\n",
    "**itself** among its neighbours would match at similarity 1.0 and vote for its own correct answer β€”\n",
    "which would push every number in this section towards perfect while measuring nothing. `-np.inf` on\n",
    "the self-match is what prevents that. An uploaded file is not in the index, so it cannot be its own\n",
    "neighbour, and `family_model.knn_vote` therefore omits the mask at serving time: the one deliberate\n",
    "difference between how this is trained and how it runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "120b527a",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "feature row width: 60"
     ]
    }
   ],
   "source": [
    "def knn_features(train_idx, rows, k=20):\n",
    "    \"\"\"Per-class weighted neighbour vote, using ONLY training documents as the index.\"\"\"\n",
    "    idx = np.asarray(train_idx)\n",
    "    sims = E[rows] @ E[idx].T\n",
    "    sims = np.where(rows[:, None] == idx[None, :], -np.inf, sims)   # never its own neighbour\n",
    "    top = np.argpartition(-sims, kth=min(k, sims.shape[1] - 1), axis=1)[:, :k]\n",
    "\n",
    "    out = np.zeros((len(rows), len(CLASSES)), np.float32)\n",
    "    for r in range(len(rows)):\n",
    "        cols = top[r]\n",
    "        for c, w in zip(idx[cols], np.clip(sims[r, cols], 0, None)):\n",
    "            out[r, y_idx[c]] += w\n",
    "    tot = out.sum(1, keepdims=True)\n",
    "    return out / np.where(tot == 0, 1, tot)\n",
    "\n",
    "def build_X(train_idx, rows):\n",
    "    \"\"\"The full feature matrix. Order matters - `family_model.build_features` mirrors it exactly.\"\"\"\n",
    "    return np.c_[M[rows], mimo_fam[rows], mimo_extra[rows], N[rows],\n",
    "                 knn_features(train_idx, rows)]\n",
    "\n",
    "all_idx = np.arange(len(y_idx))\n",
    "print('feature row width:', build_X(all_idx[:50], all_idx[:2]).shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "fa61a4d3",
   "source": [
    "### 2.4 β€” How it is scored\n",
    "\n",
    "**Everything below is out-of-fold.** The 1,100 documents are split into five folds; each fold is\n",
    "predicted by a model that never saw it during training. A number produced any other way would\n",
    "describe how well the model memorises, which is not a question anyone cares about.\n",
    "\n",
    "Four metrics, because \"accuracy\" alone hides the thing the app actually needs:\n",
    "\n",
    "| Metric | What it asks |\n",
    "|---|---|\n",
    "| **P@1** | is the top-ranked family the correct one? |\n",
    "| **P@3** | is the correct family anywhere in the short-list the app shows? |\n",
    "| **MRR** | how far down the ranking is the correct answer, on average? |\n",
    "| **macro-F1** | averaged *per class*, so a rare family being abandoned cannot hide behind a common one |\n",
    "\n",
    "The floor for all of them is the `majority / prior` row: 18.2% of the corpus is clean, so a model\n",
    "that ignores the file and always answers `none` scores **P@1 0.182**. Every other row is read\n",
    "against that, not against zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "60d77d22",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "baselines - each signal on its own\n",
      "  majority / prior             P@1=0.182  P@3=0.330  MRR=0.336  NDCG@3=0.266  macroF1=0.024\n",
      "  regex markers only           P@1=0.669  P@3=0.818  MRR=0.758  NDCG@3=0.763  macroF1=0.774\n",
      "  MiMo family only             P@1=0.498  P@3=0.585  MRR=0.597  NDCG@3=0.551  macroF1=0.441\n",
      "  embedding kNN only           P@1=0.527  P@3=0.705  MRR=0.651  NDCG@3=0.631  macroF1=0.478\n",
      "\n",
      "fused\n",
      "  logistic regression          P@1=0.992  P@3=0.998  MRR=0.995  NDCG@3=0.996  macroF1=0.992\n",
      "  gradient boosting            P@1=0.994  P@3=0.999  MRR=0.996  NDCG@3=0.997  macroF1=0.995\n",
      "\n",
      "ablations (gradient boosting)\n",
      "  GB without markers           P@1=0.691  P@3=0.823  MRR=0.778  NDCG@3=0.768  macroF1=0.666\n",
      "  GB without MiMo              P@1=0.993  P@3=0.999  MRR=0.996  NDCG@3=0.997  macroF1=0.994\n",
      "  GB: kNN + doc shape only     P@1=0.532  P@3=0.689  MRR=0.649  NDCG@3=0.625  macroF1=0.481\n",
      "\n",
      "                            P@1    P@3    MRR  NDCG@3  macroF1\n",
      "gradient boosting         0.994  0.999  0.996   0.997    0.995\n",
      "GB without MiMo           0.993  0.999  0.996   0.997    0.994\n",
      "logistic regression       0.992  0.998  0.995   0.996    0.992\n",
      "GB without markers        0.691  0.823  0.778   0.768    0.666\n",
      "regex markers only        0.669  0.818  0.758   0.763    0.774\n",
      "GB: kNN + doc shape only  0.532  0.689  0.649   0.625    0.481\n",
      "embedding kNN only        0.527  0.705  0.651   0.631    0.478\n",
      "MiMo family only          0.498  0.585  0.597   0.551    0.441\n",
      "majority / prior          0.182  0.330  0.336   0.266    0.024"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "folds = list(skf.split(np.zeros(len(y_idx)), y_idx))\n",
    "results, oof = {}, {}\n",
    "\n",
    "def rank_metrics(proba, truth):\n",
    "    order = np.argsort(-proba, axis=1)\n",
    "    ranks = np.array([np.where(order[i] == truth[i])[0][0] for i in range(len(truth))])\n",
    "    return {'P@1': float((ranks == 0).mean()), 'P@3': float((ranks < 3).mean()),\n",
    "            'MRR': float((1 / (ranks + 1)).mean()),\n",
    "            'NDCG@3': float(np.mean(np.where(ranks < 3, 1 / np.log2(ranks + 2), 0.0)))}\n",
    "\n",
    "def evaluate(name, predict_fn):\n",
    "    P = np.zeros((len(y_idx), len(CLASSES)), np.float32)\n",
    "    for tr, te in folds:\n",
    "        P[te] = predict_fn(tr, te)\n",
    "    m = rank_metrics(P, y_idx)\n",
    "    m['macroF1'] = float(f1_score(y_idx, P.argmax(1), average='macro'))\n",
    "    results[name] = m\n",
    "    oof[name] = P\n",
    "    print(f'  {name:28s} ' + '  '.join(f'{k}={v:.3f}' for k, v in m.items()))\n",
    "    return P\n",
    "\n",
    "def prior(tr, te):\n",
    "    p = np.bincount(y_idx[tr], minlength=len(CLASSES)).astype(np.float32)\n",
    "    return np.tile(p / p.sum(), (len(te), 1))\n",
    "\n",
    "def markers_only(tr, te):\n",
    "    return M[te] @ np.eye(len(FAM_LIST), len(CLASSES), dtype=np.float32) + 1e-6\n",
    "\n",
    "def mimo_only(tr, te):\n",
    "    return mimo_fam[te] + 1e-6\n",
    "\n",
    "def knn_only(tr, te):\n",
    "    return knn_features(tr, te) + 1e-6\n",
    "\n",
    "def fit_gb(Xtr, ytr, Xte):\n",
    "    clf = HistGradientBoostingClassifier(max_iter=300, learning_rate=0.08, random_state=SEED)\n",
    "    clf.fit(Xtr, ytr)\n",
    "    p = np.zeros((len(Xte), len(CLASSES)), np.float32)\n",
    "    p[:, clf.classes_] = clf.predict_proba(Xte)\n",
    "    return p\n",
    "\n",
    "def logreg(tr, te):\n",
    "    Xtr, Xte = build_X(tr, tr), build_X(tr, te)\n",
    "    sc = StandardScaler().fit(Xtr)\n",
    "    clf = LogisticRegression(max_iter=2000, C=1.0, class_weight='balanced')\n",
    "    clf.fit(sc.transform(Xtr), y_idx[tr])\n",
    "    p = np.zeros((len(te), len(CLASSES)), np.float32)\n",
    "    p[:, clf.classes_] = clf.predict_proba(sc.transform(Xte))\n",
    "    return p\n",
    "\n",
    "def gbm(tr, te):\n",
    "    return fit_gb(build_X(tr, tr), y_idx[tr], build_X(tr, te))\n",
    "\n",
    "def ablate(block):\n",
    "    lo, hi = BLOCKS[block]\n",
    "\n",
    "    def f(tr, te):\n",
    "        Xtr, Xte = build_X(tr, tr), build_X(tr, te)\n",
    "        keep = [i for i in range(Xtr.shape[1]) if not (lo <= i < hi)]\n",
    "        return fit_gb(Xtr[:, keep], y_idx[tr], Xte[:, keep])\n",
    "    return f\n",
    "\n",
    "def knn_plus_shape(tr, te):\n",
    "    return fit_gb(np.c_[knn_features(tr, tr), N[tr]], y_idx[tr],\n",
    "                  np.c_[knn_features(tr, te), N[te]])\n",
    "\n",
    "print('baselines - each signal on its own')\n",
    "evaluate('majority / prior', prior)\n",
    "evaluate('regex markers only', markers_only)\n",
    "evaluate('MiMo family only', mimo_only)\n",
    "evaluate('embedding kNN only', knn_only)\n",
    "print('\\nfused')\n",
    "evaluate('logistic regression', logreg)\n",
    "evaluate('gradient boosting', gbm)\n",
    "print('\\nablations (gradient boosting)')\n",
    "evaluate('GB without markers', ablate('markers'))\n",
    "evaluate('GB without MiMo', ablate('mimo'))\n",
    "evaluate('GB: kNN + doc shape only', knn_plus_shape)\n",
    "\n",
    "summary = pd.DataFrame(results).T.sort_values('P@1', ascending=False)\n",
    "print()\n",
    "print(summary.round(3).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "2cec7ed9",
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# A small fixed palette, assigned by what a bar MEANS rather than by its position: grey is the\n",
    "# do-nothing floor, blue is a fitted model, orange is the LLM's own opinion, aqua the embeddings.\n",
    "SURFACE, INK, INK2, GRID = '#fcfcfb', '#0b0b0b', '#52514e', '#d9d8d3'\n",
    "BLUE, ORANGE, AQUA, GREY = '#2a78d6', '#eb6834', '#1baf7a', '#8d8b85'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'figure.facecolor': SURFACE, 'axes.facecolor': SURFACE, 'savefig.facecolor': SURFACE,\n",
    "    'text.color': INK, 'axes.labelcolor': INK2, 'xtick.color': INK2, 'ytick.color': INK2,\n",
    "    'axes.edgecolor': GRID, 'grid.color': GRID, 'grid.linewidth': 0.8,\n",
    "    'font.size': 9.5, 'axes.titlesize': 11, 'axes.titleweight': 'bold',\n",
    "    'axes.spines.top': False, 'axes.spines.right': False, 'figure.dpi': 150})\n",
    "\n",
    "FIGS = Path('figures')\n",
    "FIGS.mkdir(exist_ok=True)\n",
    "\n",
    "def tidy(ax, xlabel=None):\n",
    "    ax.grid(axis='x', alpha=0.7)\n",
    "    ax.set_axisbelow(True)\n",
    "    if xlabel:\n",
    "        ax.set_xlabel(xlabel)\n",
    "\n",
    "def show(fig, name):\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(FIGS / f'{name}.png', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "e2f2a83f",
   "source": [
    "Chart 1 puts the baselines and the fusion side by side. Each of the three real signals lands\n",
    "somewhere between 0.50 and 0.67 on its own β€” better than the floor, useless as a product β€” and the\n",
    "fusion clears 0.99."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "73121107",
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "source": [
    "names  = ['majority / prior', 'MiMo family only', 'embedding kNN only', 'regex markers only',\n",
    "          'gradient boosting']\n",
    "labels = [\"always answer 'none'\\n(the floor)\", \"MiMo's family guess\", 'embedding neighbours',\n",

    "          'regex signatures', 'all four, fused']\n",

    "cols   = [GREY, ORANGE, AQUA, BLUE, BLUE]\n",

    "vals   = [results[n]['P@1'] for n in names]\n",

    "\n",

    "fig, ax = plt.subplots(figsize=(7.4, 3.5))\n",

    "y = np.arange(len(names))\n",

    "bars = ax.barh(y, vals, height=0.62, color=cols)\n",

    "bars[-1].set_edgecolor(INK); bars[-1].set_linewidth(1.2)\n",

    "for yy, v in zip(y, vals):\n",

    "    ax.text(v + 0.012, yy, f'{v:.3f}', va='center', color=INK,\n",

    "            fontweight='bold' if v == max(vals) else 'normal')\n",

    "ax.set_yticks(y, labels); ax.set_xlim(0, 1.08)\n",

    "ax.set_title('Chart 1 - no single signal is close; the fusion is')\n",

    "tidy(ax, 'P@1  (top pick correct, out of fold)')\n",

    "show(fig, 'fig1_signals')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "6003391e",

   "source": [

    "### 2.5 β€” Why 0.994 is the least interesting number here\n",

    "\n",

    "A model that scores 0.994 on a coursework corpus should be treated as a bug report until proven\n",

    "otherwise, so the next thing to do is take the model apart and find out which block is carrying it.\n",

    "\n",

    "Chart 2 removes one block at a time and re-runs the whole out-of-fold procedure."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "f0766044",

   "outputs": [

    {

     "output_type": "display_data",

     "metadata": {},

     "data": {

      "image/png": 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"

     }

    }

   ],

   "source": [

    "abl    = ['gradient boosting', 'GB without MiMo', 'GB without markers', 'GB: kNN + doc shape only']\n",

    "labels = ['full model', \"without MiMo's guess\", 'without regex signatures',\n",
    "          'neighbours + shape only']\n",
    "vals   = [results[n]['P@1'] for n in abl]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.4, 3.1))\n",
    "y = np.arange(len(abl))\n",
    "ax.barh(y, vals, height=0.6, color=[BLUE, BLUE, ORANGE, GREY])\n",
    "for yy, v in zip(y, vals):\n",
    "    ax.text(v + 0.012, yy, f'{v:.3f}', va='center', color=INK)\n",
    "ax.axvline(results['gradient boosting']['P@1'], color=INK, lw=1, ls=':', alpha=0.55)\n",
    "ax.set_yticks(y, labels); ax.set_xlim(0, 1.08)\n",
    "ax.set_title('Chart 2 - remove one block at a time: only the signatures matter')\n",
    "tidy(ax, 'P@1  (top pick correct, out of fold)')\n",
    "show(fig, 'fig2_ablation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "8c97576b",
   "source": [
    "Two results, and the second is the important one.\n",
    "\n",
    "**MiMo's opinion is nearly redundant.** Removing it costs 0.001 β€” the model reaches 0.993 without\n",
    "ever being told what the language model thought. The signal it provides is almost entirely\n",
    "recoverable from the other three.\n",
    "\n",
    "**The structural signatures are carrying almost everything.** Without them the model falls from\n",
    "0.994 to **0.691**, and that is not a flaw in the model, it is a fact about the corpus: the label\n",
    "*is* the inserted marker. A document is labelled `ransomware_simulation` **because** the generator\n",
    "wrote a `RANSIM TEST` string into it, and the signature block reads that string back out. The model\n",
    "is being scored partly on its ability to notice a label that was written into the file.\n",
    "\n",
    "So there are two honest numbers, and they describe two different situations:\n",
    "\n",
    "| | P@1 | Describes |\n",
    "|---|---|---|\n",
    "| with signatures | **0.994** | a file carrying one of the twelve payload shapes this project generated |\n",
    "| without signatures | **0.691** | a file carrying something else β€” the harder, more realistic case |\n",
    "\n",
    "**The app reports whichever one applies to the file in front of it.** `family_model.predict` checks\n",
    "whether the upload carries a known signature and quotes 0.994 or 0.691 accordingly, rather than\n",
    "printing the flattering number in both cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "cfd8d2d7",
   "source": [
    "### 2.6 β€” Which families actually depend on the signatures\n",
    "\n",
    "The ablation is an average, and averages hide the shape of a failure. Chart 3 splits it by family:\n",
    "blue is the full model, orange is the same model with the signature block removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "28cdd585",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "weakest family without signatures: ssrf (recall 0.35)"
     ]
    },
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "source": [
    "full = oof['gradient boosting'].argmax(1)\n",
    "nomk = oof['GB without markers'].argmax(1)\n",
    "\n",
    "def recall_by_class(pred):\n",
    "    return np.array([(pred[y_idx == c] == c).mean() for c in range(len(CLASSES))])\n",
    "\n",
    "r_full, r_nomk = recall_by_class(full), recall_by_class(nomk)\n",
    "order = np.argsort(r_nomk)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.8, 5.0))\n",
    "y = np.arange(len(CLASSES))\n",
    "ax.barh(y + 0.2, r_full[order], height=0.38, color=BLUE, label='full model')\n",
    "ax.barh(y - 0.2, r_nomk[order], height=0.38, color=ORANGE, label='without regex signatures')\n",
    "ax.set_yticks(y, [CLASSES[i] for i in order]); ax.set_xlim(0, 1.05)\n",
    "ax.set_title('Chart 3 - per-family recall: where the signatures are load-bearing')\n",
    "tidy(ax, 'recall within the family (out of fold)')\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10), ncol=2, frameon=False)\n",
    "show(fig, 'fig3_per_family')\n",
    "\n",
    "print('weakest family without signatures:', CLASSES[int(np.argmin(r_nomk))],\n",
    "      f'(recall {r_nomk.min():.2f})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "id": "cba0eb6b",
   "source": [
    "The spread is wide. `steganographic_payload`, `uri_redirect_phishing` and `dde_template_injection`\n",
    "survive the loss of their signatures well β€” the document shape and the neighbours still identify\n",
    "them. `ssrf` collapses to **0.35**, the worst of the thirteen, and the reason is visible in the\n",
    "data: an SSRF payload is a URL pointing at a cloud metadata address. It adds no distinctive object\n",
    "to the PDF and it reads like ordinary document text, so with the signature gone there is very little\n",
    "left to recognise.\n",
    "\n",
    "Chart 4 shows where those mistakes go, for the honest (no-signature) model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "id": "522e5dfb",
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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     }
    }
   ],
   "source": [
    "cm = np.zeros((len(CLASSES), len(CLASSES)), int)\n",
    "for t, p in zip(y_idx, nomk):\n",
    "    cm[t, p] += 1\n",
    "cmn = cm / np.maximum(cm.sum(1, keepdims=True), 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.6, 6.4))\n",
    "cmap = matplotlib.colors.LinearSegmentedColormap.from_list('seq', ['#ffffff', BLUE])\n",
    "im = ax.imshow(cmn, cmap=cmap, vmin=0, vmax=1)\n",
    "ax.set_xticks(range(len(CLASSES)), CLASSES, rotation=90)\n",
    "ax.set_yticks(range(len(CLASSES)), CLASSES)\n",
    "ax.set_xlabel('predicted'); ax.set_ylabel('true family')\n",
    "for i in range(len(CLASSES)):\n",
    "    for j in range(len(CLASSES)):\n",
    "        if cm[i, j]:\n",
    "            ax.text(j, i, cm[i, j], ha='center', va='center', fontsize=7.5,\n",
    "                    color='#ffffff' if cmn[i, j] > 0.55 else INK2)\n",
    "ax.set_title('Chart 4 - where the honest model (no signatures) confuses families')\n",
    "fig.colorbar(im, ax=ax, shrink=0.72, label=\"share of the true family's documents\")\n",

    "ax.grid(False)\n",

    "show(fig, 'fig4_confusion')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "8ff9c304",

   "source": [

    "The confusions are not random, and they corroborate something the corpus documentation already\n",

    "says. `ransomware_simulation` is mistaken for `javascript_injection` 15 times and\n",

    "`object_action_injection` for `shellcode_embedded_exe` 13 times β€” and those are precisely the pairs\n",

    "the generator gives a shared JavaScript launcher to. The model is confusing documents that genuinely\n",

    "do resemble each other.\n",

    "\n",

    "The `none` row is the reassuring one: 190 of 200 clean files are still correctly called clean\n",

    "without any signature evidence at all. **The model is much better at \"is this clean?\" than at \"which\n",

    "of twelve attacks is this?\"** β€” which is the same division of competence MiMo shows, arrived at\n",

    "independently.\n",

    "\n",

    "This is also the argument for a ranked short-list instead of one answer. When the top pick is wrong,\n",

    "P@3 says the right family is usually still in the list: 0.823 even in the no-signature regime. An\n",

    "interface that shows three candidates with confidences is honest about that; one that asserts a\n",

    "single family is not.\n",

    "\n",

    "### 2.7 β€” Fit on everything, and export the file the app loads\n",

    "\n",

    "The figures above describe the cross-validated procedure. The model that ships is refitted on all\n",

    "1,100 documents, since at serving time there is no held-out set left to protect.\n",

    "\n",

    "**The estimator and its schema travel in one file.** `family_naming_model.pkl` contains both the\n",

    "fitted trees and a `spec` recording the class order, the family list, the exact numeric columns, the\n",

    "neighbour count and the embedding model β€” everything `family_model.py` needs in order to rebuild an\n",

    "identical feature row. It asserts every one of those fields at import, so a retrain that changes the\n",

    "layout fails loudly at startup instead of quietly scoring the wrong columns.\n",

    "\n",

    "The scikit-learn version is recorded in the same file and pinned in `requirements.txt`. A pickled\n",

    "estimator is only reliably readable by the version that wrote it."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "b3ed9759",

   "outputs": [

    {

     "output_type": "stream",

     "name": "stdout",

     "text": [

      "fitted on (1100, 60) with scikit-learn 1.7.2\n",

      "wrote family_naming_model.pkl  (915.2 KB)"

     ]

    }

   ],

   "source": [

    "import joblib\n",

    "\n",

    "X_full = build_X(all_idx, all_idx)\n",

    "final = HistGradientBoostingClassifier(max_iter=300, learning_rate=0.08, random_state=SEED)\n",

    "final.fit(X_full, y_idx)\n",

    "\n",

    "spec = {\n",

    "    'classes': CLASSES,\n",

    "    'families': FAM_LIST,\n",

    "    'numeric_features': NUM,\n",

    "    'knn_k': 20,\n",

    "    'feature_order': ['markers', 'mimo_family_onehot', 'mimo_pred_injected',\n",

    "                      'mimo_parse_ok', 'numeric', 'numeric_log', 'knn_vote'],\n",

    "    'blocks': {k: list(v) for k, v in BLOCKS.items()},\n",

    "    'embedder': {'repo': 'nomic-ai/nomic-embed-text-v1.5',\n",

    "                 'prefix': 'search_document: ', 'dims': 768, 'normalised': True},\n",

    "    'metrics_out_of_fold': results,\n",

    "    'note': ('P@1 0.994 with marker features; 0.691 without them. The corpus label IS the '\n",

    "             'inserted marker, so the high figure describes files of these twelve families '\n",

    "             'and the lower one better predicts behaviour on anything else.'),\n",

    "}\n",

    "\n",

    "# One file, not two. The estimator and the schema it expects travel together, because a model\n",

    "# whose feature layout has to be guessed from a second file that may or may not have been copied\n",

    "# alongside it is a model that will eventually be fed the wrong columns in the right shape.\n",

    "import sklearn\n",

    "spec['sklearn_version'] = sklearn.__version__\n",

    "\n",

    "# joblib rather than plain pickle, for the compression: the raw bundle is 3.6 MB of mostly\n",

    "# repetitive tree arrays and comes down to well under one.\n",

    "joblib.dump({'model': final, 'spec': spec}, MODEL_FILE, compress=3)\n",

    "\n",

    "print('fitted on', X_full.shape, 'with scikit-learn', sklearn.__version__)\n",

    "print(f'wrote {MODEL_FILE}  ({Path(MODEL_FILE).stat().st_size / 1024:.1f} KB)')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "d608b370",

   "source": [

    "### 2.8 β€” The contract the app has to honour\n",

    "\n",

    "Everything after this point is the application, and it inherits three obligations from this part:\n",

    "\n",

    "1. **Rebuild the same 17 document-shape measurements from an uploaded PDF.** They came from the EDA\n",

    "   parquet here; at serving time there is only a file. This is what `doc_features.py` is for, and\n",

    "   why it is copied from the EDA notebook rather than rewritten.\n",

    "2. **Embed the query with the same model and prefix the index was built with**, or the neighbour\n",

    "   vote lands in a different space and votes confidently for nothing.\n",

    "3. **Report the regime.** 0.994 and 0.691 are both true; only one of them is true about the file on\n",

    "   screen.\n",

    "\n",

    "The next part is the rule that keeps all three honest."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## 3 β€” The provenance rule\n",

    "\n",

    "The published numbers only describe this app if the app reads a PDF and asks the question exactly\n",

    "as the notebooks did. So the code was lifted from the **dataset repos themselves**, not rewritten\n",

    "and not copied from the earlier archived app:\n",

    "\n",

    "| Borrowed | From |\n",

    "|---|---|\n",

    "| `build_skeleton`, `mask_leaks`, `detect_markers`, `payload_window`, `ANY_MARKER_RE`, `INJECTION_MARKERS`, `LEAK_STRINGS` | EDA notebook, cells 88 / 89 / 107 |\n",

    "| `SYSTEM`, `PREFILL`, `build_messages`, `scan_objects`, `parse_response`, `MAX_NEW`, `BATCH` | Part B notebook, cell 48 |\n",

    "| embedder repo, prefix, dims, normalisation, input column | `Part_A_Outputs/part_a_results.json` |\n",

    "\n",

    "One decision worth stating: **the parser is the narrow original**, not the widened version that\n",

    "appears in the archived app. A wider salvage recovers more verdicts and would also mean the F1\n",

    "quoted in the interface describes a parser that is not the one running.\n"

   ],

   "id": "b715434c"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 3.1 β€” The prompt and the prefill, as deployed\n"

   ],

   "id": "6b76173a"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import mimo\n",

    "\n",

    "print(f'system message : {len(mimo.SYSTEM)} chars')\n",

    "print(f'families       : {len(mimo.FAMILIES)} (closed set, fixed order)')\n",

    "print(f'max new tokens : {mimo.MAX_NEW}')\n",

    "print(f'batch          : {mimo.BATCH}')\n",

    "print()\n",

    "print('prefill:', repr(mimo.PREFILL))\n"

   ],

   "id": "0b6aa2fb"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "MiMo is the only model in Part B that carries a prefill. It is reasoning-trained and opens every\n",

    "answer with `<think>`; at a 200-token budget it never closed the block, so not one of its 1,100\n",

    "answers reached the JSON. An empty, already-closed think-block says the deliberation is finished\n",

    "before it begins, and the opening brace puts the model inside the answer.\n"

   ],

   "id": "1760bc69"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 3.2 β€” The parser survives the bug that mattered\n",

    "\n",

    "Part B's first parser used a regex to find the JSON object. Every injected file in this corpus\n",

    "carries an EICAR-style marker containing a closing brace, so the moment a model quoted its\n",

    "evidence the match truncated and the verdict was thrown away β€” **the bug fired exactly when the\n",

    "model was right**. The replacement counts braces and tracks string literals.\n"

   ],

   "id": "ae603852"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import json\n",

    "\n",

    "evidence = r'X5O!P%@AP[4\\PZX54(P^)7CC)7}$EICAR-STANDARD-ANTIVIRUS-TEST-FILE'\n",

    "body = json.dumps({'injected': True, 'injection_type': 'javascript_injection',\n",

    "                   'evidence': evidence, 'reasoning': 'marker present'})\n",

    "\n",

    "# what MiMo actually emits: the prefill, then the remainder of that object\n",

    "raw = mimo.PREFILL + body[body.index(':') + 1:]\n",

    "r = mimo.parse_response(raw)\n",

    "\n",

    "print('route                    :', r['parsed_by'])\n",

    "print('verdict / family         :', r['pred_injected'], r['pred_family'])\n",

    "print('brace inside evidence ok :', '}' in r['evidence'])\n",

    "\n",

    "# reasoning-trained models restate the schema while thinking: the LAST object must win\n",

    "two = '{\"injected\": false, \"injection_type\": \"none\"} ... then really ' + raw\n",

    "print('last-object-wins         :', mimo.parse_response(two)['pred_injected'] == 1)\n"

   ],

   "id": "2cca1528"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## 4 β€” Fidelity: does this read a PDF the way the corpus was read?\n",

    "\n",

    "This is the check the whole design rests on. It pulls real PDFs out of the generation repo, runs\n",

    "them through the deployed `corpus_text.py`, and compares against the published parquet **character\n",

    "by character** β€” not 'close enough', identical. Injected and clean files both, because masking and\n",

    "binary-stream handling differ between them and a test that saw only one would pass on a broken\n",

    "extractor.\n"

   ],

   "id": "69a6c6aa"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "!python test_fidelity.py 8\n"

   ],

   "id": "850daba7"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## 5 β€” The embedding lookup, and the silent failure it avoids\n",

    "\n",

    "A query embedded with the wrong model, or without Part A's `search_document: ` prefix, lands in a\n",

    "different vector space and returns meaningless neighbours β€” silently, with no error anywhere. So\n",

    "`check_provenance()` asserts the embedder repo, prefix, dimension, normalisation and input column\n",

    "against Part A's own results file before any lookup runs.\n"

   ],

   "id": "2b8633f1"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import neighbours\n",

    "\n",

    "print('Part A winner, asserted against part_a_results.json:')\n",

    "for k, v in neighbours.check_provenance().items():\n",

    "    print(f'  {k:15s}: {v}')\n",

    "\n",

    "idx = neighbours.load_index()\n",

    "print()\n",

    "print('index:', idx['matrix'].shape, idx['matrix'].dtype)\n"

   ],

   "id": "f8aa89e8"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 5.1 β€” Proof the query lands in the index's own space\n",

    "\n",

    "Embed a corpus row's `payload_window` and dot it against that same row's stored vector. Anything\n",

    "below 1.0 means the app and the index disagree about what an embedding is.\n"

   ],

   "id": "9f3eafaa"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import pandas as pd\n",

    "\n",

    "CORPUS = ('https://huggingface.co/datasets/Cyber-security-final-project/'\n",

    "          'HARMLESS_Synthetic_Injected_PDFs_EDA/resolve/main/Datasets/'\n",

    "          'synthetic_corpus_part2_clustered.parquet')\n",

    "\n",

    "corpus = pd.read_parquet(CORPUS)\n",

    "pos = {str(f): i for i, f in enumerate(idx['ids'])}\n",

    "\n",

    "sample = corpus[['file_id', 'payload_window', 'injection_type']].sample(5, random_state=0)\n",

    "for fid, window, family in sample.itertuples(index=False):\n",

    "    self_sim = float(neighbours.embed(window) @ idx['matrix'][pos[fid]])\n",

    "    print(f'{family[:24]:24s} self-cosine = {self_sim:.4f}')\n"

   ],

   "id": "fda48549"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Measured: **1.0000 on every file**. The query vectors are literally the index vectors.\n",

    "\n",

    "What the lookup is worth is a separate question, and a smaller number: Part A's winning embedder\n",

    "reaches **precision@5 of 35.6%** against a 6.8% random baseline. Fewer than 2 of the 5 files\n",

    "returned are the same kind of attack. Far better than chance, and not good β€” which is why the\n",

    "interface says *nearest files in the corpus* and never *the same attack*.\n"

   ],

   "id": "c5cc7dd0"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## 6 β€” Fitting the free tier: what was forced, not chosen\n",

    "\n",

    "### 6.1 β€” MiMo cannot be called remotely\n",

    "\n",

    "The first plan was to run the app on a free CPU Space and call MiMo through an inference provider.\n",

    "That is not available at any price short of a dedicated endpoint:\n"

   ],

   "id": "69461017"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 6.0 β€” Why MiMo, when Gemma scored higher\n",

    "\n",

    "Part B's actual winner is **Gemma-2-9B at F1 0.969**; this app runs **MiMo-7B at F1 0.945**. That\n",

    "is a hosting decision, not a disagreement with the evaluation. Three constraints a free Space\n",

    "cannot absorb:\n",

    "\n",

    "- **Gemma is gated.** It needs an account with Google's licence accepted plus a read token, so on\n",

    "  a public Space the first thing a new visitor meets is a 403 β€” or the app has to ask strangers to\n",

    "  paste a token. MiMo downloads for anyone, with no account at all.\n",

    "- **Gemma is 2.6x slower** β€” 10.95 s per window against MiMo's 4.18 s, measured in Part B on the\n",

    "  same T4. A ZeroGPU grant is capped at 300 s and the scan *plus* the model load must fit inside\n",

    "  it, so the slower model means roughly a third as many regions per run.\n",

    "- **Free ZeroGPU is ~5 minutes per day.** At Gemma's rate that is a couple of batches for a whole\n",

    "  day. Gemma is also ~6 GB in 4-bit against MiMo's ~5 GB β€” not decisive alone, but it points the\n",

    "  same way.\n",

    "\n",

    "The price is **0.024 F1**, and family-naming falling from 63% to 43%. Both are stated in the\n",

    "interface rather than rounded away. Moving to dedicated paid hardware would make Gemma a change to\n",

    "two repo constants in `mimo.py` plus a token secret β€” the prompt, prefill and parser are shared\n",

    "and would not need touching.\n",

    "\n",

    "The cell in section 7 prints all four models' Part B scores, so the trade is visible as numbers.\n"

   ],

   "id": "9e5e44b1"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import requests\n",

    "\n",

    "for repo in ['XiaomiMiMo/MiMo-7B-RL', 'Qwen/Qwen2.5-7B-Instruct']:\n",

    "    r = requests.get(f'https://huggingface.co/api/models/{repo}',\n",

    "                     params={'expand[]': 'inferenceProviderMapping'}).json()\n",

    "    providers = r.get('inferenceProviderMapping') or {}\n",

    "    print(f'{repo:32s} providers:', list(providers) or 'NONE')\n"

   ],

   "id": "a3ccc03d"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "960ce404",

   "source": [

    "So the weights run locally, and the only question left was on what.\n",

    "\n",

    "### 6.2 β€” One runtime\n",

    "\n",

    "**ZeroGPU, running the evaluation's own configuration**: the BF16 checkpoint quantised to 4-bit NF4\n",

    "by `bitsandbytes`, greedy, 200 new tokens, batched at 8, the whole scan inside one `@spaces.GPU`\n",

    "call. Nothing about the arithmetic differs from the run that produced F1 0.945.\n",

    "\n",

    "There is no `gpu`/`cpu` control. A CPU path through `llama.cpp` was attempted, so that the Space\n",

    "could keep working once a visitor's daily GPU quota ran out, and it cannot be installed here at all:\n",

    "the prebuilt `llama-cpp-python` wheels are tagged `linux_x86_64` but linked against **musl** while a\n",

    "Space runs on glibc, and PyPI ships no binary wheel, so the sdist has to compile β€” which exceeded\n",

    "the build limit with `Job timeout`. Shipping a control whose second option always fails is worse\n",

    "than shipping one option, so the app offers one. `requirements.txt` records both attempts, to save\n",

    "the next person the afternoon."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "7da9512b",

   "outputs": [],

   "source": [

    "print('GPU present here      :', mimo.GPU_PRESENT)\n",

    "print('seconds per region    :', mimo.SECONDS_PER_WINDOW)\n",

    "print('GPU grant requested   :', mimo.GPU_DURATION, 's ->',\n",

    "      int(mimo.GPU_DURATION * 1.5), 's reserved by the scheduler')\n",

    "print('regions per GPU batch :', mimo.MAX_WINDOWS_GPU)\n",

    "print()\n",

    "print(mimo.RUNTIME_NOTE)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 6.3 β€” Batching, and why the user picks the batch\n",

    "\n",

    "Part B scored one window per document, because it already knew where the payload was. An uploaded\n",

    "file offers no such promise, and neither runtime can read every window of a real PDF inside its\n",

    "limit. So:\n",

    "\n",

    "1. the marker alternation that located the corpus payload is run over the **whole** skeleton, not\n",

    "   stopped at the first hit; every match becomes a candidate with the identical +/-1,500-character\n",

    "   shape, and overlapping ones are merged;\n",

    "2. the rest of the document is tiled into windows of the same size;\n",

    "3. the list is cut into batches sized to fit one run, and **you choose which batch to spend a run\n",

    "   on**. The report always states how much is still unread.\n",

    "\n",

    "Ranking decides reading order, never the verdict.\n"

   ],

   "id": "14e881f9"

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import corpus_text\n",

    "from huggingface_hub import hf_hub_download\n",

    "\n",

    "path = hf_hub_download('Cyber-security-final-project/Generated_Injected_PDFs_HARMLESS',\n",

    "                       'Output_PDFs/javascript_injection_WICAR_0001.pdf', repo_type='dataset')\n",

    "skeleton, truncated, dropped = corpus_text.build_skeleton(open(path, 'rb').read())\n",

    "\n",

    "for cover_all in (False, True):\n",

    "    regions = corpus_text.candidate_windows(skeleton, cover_all=cover_all)\n",

    "    print(f'cover_all={str(cover_all):5s} -> {len(regions)} region(s):',\n",

    "          [w['source'] for w in regions])\n"

   ],

   "id": "c32401fd"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### 6.4 β€” The sweep regions are honestly worse, and the app says so\n",

    "\n",

    "Marker-only triage leaves most of a file unread, and the marker set only knows the twelve families\n",

    "this project generated β€” so a payload shaped like none of them would sit in text the model never\n",

    "saw while the report said *clean*. The sweep closes that hole. It also introduces a real problem.\n",

    "\n",

    "Part B only ever showed MiMo marker-centred windows or the head of a document. Handed an arbitrary\n",

    "mid-file content stream β€” a page of font-positioning operators β€” **MiMo does not answer**: it\n",

    "carries on copying the input after the prefill, and the response parses as unrecoverable, which\n",

    "scores as *not injected*.\n",

    "\n",

    "Observed live on one injected corpus PDF:\n",

    "\n",

    "| region | kind | verdict | evidence |\n",

    "|---|---|---|---|\n",

    "| 1 | marker | **PAYLOAD**, `javascript_injection` | `/JS (var payload = 'eicar-standard-...')` |\n",

    "| 2-6 | sweep | clean *(unreadable answer)* | β€” |\n",

    "\n",

    "So: sweep regions buy coverage of text that would otherwise never be looked at, and a *clean*\n",

    "verdict on one is close to no evidence at all. Rows are labelled `marker` / `sweep`, the report\n",

    "counts sweep parse failures separately and explains them, and the sweep can be switched off to\n",

    "keep the app strictly inside the shape Part B measured.\n",

    "\n",

    "The tempting fixes β€” widening the parser, or editing the prompt to insist harder on JSON β€” are\n",

    "both refused on purpose. Either one would break the provenance argument that justifies quoting\n",

    "F1 0.945 anywhere in the interface.\n"

   ],

   "id": "1830d7bf"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## 7 β€” What broke on the way, and what it cost\n",

    "\n",

    "| # | Symptom | Cause | Resolution |\n",

    "|---|---|---|---|\n",

    "| 1 | no provider serves MiMo | `inferenceProviderMapping` empty for every variant | run the weights locally |\n",

    "| 2 | `Quota exceeded for flavor cpu-basic: limit=0` | the **org** has no compute allowance; its old Space only ran because it was `sdk: static` | moved to the personal Space |\n",

    "| 3 | `Textbox.__init__() got an unexpected keyword 'show_copy_button'` | no `sdk_version` pinned, so HF installed gradio 6, which removed the argument | pinned `sdk_version: 5.50.0` **and** dropped the argument |\n",

    "| 4 | `No @spaces.GPU function detected during startup` | the Space is ZeroGPU, and the CPU-only app had no decorated function | added the GPU path; the decorator is applied at import, because ZeroGPU scans at startup |\n",

    "| 5 | `requested GPU duration (450s) is larger than the maximum allowed` | the `spaces` client asks for **1.5x** the declared duration, against a 300 s cap | lowered the duration; also moved the 15.7 GB weight download *outside* the grant |\n",

    "| 6 | `Value: 0 is not in the list of choices: []` | the batch dropdown validates against choices that are empty until a PDF is uploaded | `allow_custom_value=True` plus a tolerant `resolve_batch()` |\n",

    "| 7 | `exceeded your free ZeroGPU quota (270s requested vs 264s left)` | ~5 min/day, and the scheduler **reserves** the full requested duration up front | shorter grant (110 s), smaller batch (8), and a user-facing runtime picker so the CPU path can take over |\n",

    "| 8 | `libc.musl-x86_64.so.1: cannot open shared object file` | the prebuilt `llama-cpp-python` wheels are tagged `linux_x86_64` but linked against **musl**; a Space is glibc | build from the PyPI sdist, the only binary-free route |\n",

    "\n",

    "Two of these are worth carrying into any future Space in this project: **pin `sdk_version`**, and\n",

    "**never pin `torch`** β€” the image supplies one matching its own driver.\n"

   ],

   "id": "9d4a06d1"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "da7bab95",

   "source": [

    "## 8 β€” The numbers, and what they do not cover\n",

    "\n",

    "On the 1,100-document corpus Part B measured, MiMo-7B-RL scored **F1 0.945**, precision 0.988,\n",

    "recall 0.906, naming the family correctly 43.3% of the time, with 10 false alarms on 200 clean\n",

    "files and 155 unparsable answers.\n",

    "\n",

    "**A detector that calls every file malicious scores F1 0.900 on this corpus**, because 82% of it\n",

    "is injected. Read 0.945 against 0.900, not against zero β€” it is a 5% relative improvement over\n",

    "doing no work at all. Gemma-2-9B scored 0.969 and is the real Part B winner; MiMo is used here\n",

    "because it is ungated, needs no token, and is 2.6x faster.\n",

    "\n",

    "And the corpus is synthetic: harmless EICAR/AMTSO/WICAR/RANSIM test markers injected into ordinary\n",

    "PDFs. Real malware does not announce itself the same way. **This is not a general malware\n",

    "scanner.**\n",

    "\n",

    "The family-naming model, out of fold on the same 1,100 documents: **P@1 0.994** where a known\n",

    "signature is present and **0.691** where none is, against a floor of 0.182 for always answering\n",

    "`none`. Both figures are in Part 2, and the app quotes whichever describes the file it is looking\n",

    "at. The end-to-end check below runs the deployed modules over the thirteen shipped examples."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import pandas as pd\n",

    "\n",

    "PART_B = ('https://huggingface.co/datasets/Cyber-security-final-project/'\n",

    "          'Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition/'\n",

    "          'resolve/main/Part_B_Outputs/part_b_results.json')\n",

    "print(pd.read_json(PART_B).set_index('detector').to_string())\n"

   ],

   "id": "196945ec"

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "b1996424",

   "source": [

    "### 8.1 β€” The two models, end to end on the shipped examples\n",

    "\n",

    "`corpus_text` β†’ `doc_features` β†’ `neighbours` β†’ `family_model`, which is exactly the path `app.py`\n",

    "takes, with MiMo's block stubbed to the pessimistic case (*no family, not injected*) so the trees\n",

    "get no help at all from the language model.\n",

    "\n",

    "**These thirteen files flatter the model.** Every example is a corpus document, and the corpus is\n",

    "the neighbour index β€” so each one finds *itself* among its nearest neighbours at similarity β‰ˆ 1.0,\n",

    "the self-match that Part 2.3 masked out during training and that serving cannot mask. Read the score\n",

    "below as a check that the wiring is correct, not as an accuracy figure. The honest numbers are the\n",

    "out-of-fold ones."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "5d84fb39",

   "outputs": [

    {

     "output_type": "stream",

     "name": "stdout",

     "text": [

      "Example_0  true=none                     model=none                     100.0% OK\n",

      "Example_1  true=object_action_injection  model=object_action_injection  100.0% OK\n",

      "Example_2  true=ssrf                     model=none                     50.4% MISS\n",

      "Example_3  true=dde_template_injection   model=dde_template_injection   100.0% OK\n",

      "Example_4  true=xfa_acroform_injection   model=xfa_acroform_injection   100.0% OK\n",

      "Example_5  true=ransomware_simulation    model=ransomware_simulation    100.0% OK\n",

      "Example_6  true=shellcode_embedded_exe   model=shellcode_embedded_exe   99.9% OK\n",

      "Example_7  true=cross_site_scripting     model=cross_site_scripting     100.0% OK\n",

      "Example_8  true=polyglot_file            model=polyglot_file            100.0% OK\n",

      "Example_9  true=steganographic_payload   model=steganographic_payload   100.0% OK\n",

      "Example_10 true=uri_redirect_phishing    model=uri_redirect_phishing    100.0% OK\n",

      "Example_11 true=javascript_injection     model=javascript_injection     100.0% OK\n",

      "Example_12 true=llm_prompt_injection     model=llm_prompt_injection     100.0% OK\n",

      "correct 12/13"

     ]

    }

   ],

   "source": [

    "import corpus_text, doc_features, family_model\n",

    "\n",

    "KEY = {0: 'none', 1: 'object_action_injection', 2: 'ssrf', 3: 'dde_template_injection',\n",

    "       4: 'xfa_acroform_injection', 5: 'ransomware_simulation', 6: 'shellcode_embedded_exe',\n",

    "       7: 'cross_site_scripting', 8: 'polyglot_file', 9: 'steganographic_payload',\n",

    "       10: 'uri_redirect_phishing', 11: 'javascript_injection', 12: 'llm_prompt_injection'}\n",

    "\n",

    "STUB = {'pred_family': 'none', 'pred_injected': 0, 'parse_ok': 1}   # no help from MiMo\n",

    "\n",

    "correct = 0\n",

    "for n, truth in KEY.items():\n",

    "    path = f'examples/Example_{n}.pdf'\n",

    "    data = open(path, 'rb').read()\n",

    "    skeleton, truncated, dropped = corpus_text.build_skeleton(data)\n",

    "    stats = doc_features.describe(path, data, skeleton, truncated, dropped)\n",

    "    window = corpus_text.candidate_windows(skeleton, cover_all=False)[0]\n",

    "\n",

    "    r = family_model.predict(skeleton, STUB, stats, window['text'])\n",

    "    correct += r['top'] == truth\n",

    "    print(f\"Example_{n:<2d} true={truth:24s} model={r['top']:24s} \"\n",

    "          f\"{r['confidence']:5.1%} {'OK' if r['top'] == truth else 'MISS'}\")\n",

    "\n",

    "print('correct', correct, '/13')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "3e3e7116",

   "source": [

    "The single miss is `Example_2`, the SSRF sample β€” the family Chart 3 identified as the weakest\n",

    "without signature evidence, and the model puts its confidence on `none`. That is the predicted\n",

    "failure mode showing up in the demo, which is a better outcome than a demo that hides it."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "d8acd3ca",

   "source": [

    "## 9 β€” Deploying\n",

    "\n",

    "`VCURR/HF_SPACE/` is the source of truth; the Space is a deployment target, not a working copy.\n",

    "\n",

    "`family_naming_model.pkl` is uploaded with the code. At 915 KB it belongs in the repo rather than\n",

    "being fetched at runtime, which also means stage 4 has no network dependency beyond the embedding\n",

    "model it already needed."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "882c7cdc",

   "outputs": [],

   "source": [

    "# run from VCURR/HF_SPACE/\n",

    "from huggingface_hub import HfApi, CommitOperationAdd\n",

    "\n",

    "SPACE_REPO = 'BentoUniAcc/Mimo_Injection_detector'\n",

    "\n",

    "FILES = ['README.md', 'requirements.txt', 'app.py', 'corpus_text.py', 'mimo.py',\n",

    "         'neighbours.py', 'doc_features.py', 'family_model.py', 'family_naming_model.pkl',\n",

    "         'test_fidelity.py', 'App_Creation.ipynb']\n",

    "\n",

    "api = HfApi()\n",

    "commit = api.create_commit(\n",

    "    repo_id=SPACE_REPO, repo_type='space',\n",

    "    operations=[CommitOperationAdd(f, f) for f in FILES],\n",

    "    commit_message='describe the change here')\n",

    "print(commit.commit_url)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "id": "00a992af",

   "outputs": [],

   "source": [

    "# build status\n",

    "import requests\n",

    "\n",

    "runtime = requests.get(\n",

    "    f'https://huggingface.co/api/spaces/{SPACE_REPO}').json()['runtime']\n",

    "print(runtime['stage'], '|', runtime.get('errorMessage'))"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "id": "75bb9eff",

   "source": [

    "---\n",

    "\n",

    "*Space: [BentoUniAcc/Mimo_Injection_detector](https://huggingface.co/spaces/BentoUniAcc/Mimo_Injection_detector)*"

   ]

  }

 ],

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