| { |
| "payload": "/home/hacnho/Projects/research/targets/huntr/evidence/tensorizer-write-tensor-scanner-bypass/malicious_tensorizer_write_tensor_scanner_bypass.pkl", |
| "payload_sha256": "dca5eb20916bd01c8b08a9950e4de822cdd7245376f912bc8cd455ddeebcfa1d", |
| "versions": [ |
| "tensorizer 2.12.1", |
| "numpy 2.1.3", |
| "torch 2.12.1+cpu" |
| ], |
| "picklescan_clean": true, |
| "modelscan_clean": true, |
| "load_success": true, |
| "write_proof": true, |
| "picklescan": { |
| "cmd": [ |
| "/home/hacnho/Projects/claudebounty/.claude/skills/.venv/bin/python3", |
| "-m", |
| "picklescan", |
| "-p", |
| "/home/hacnho/Projects/research/targets/huntr/evidence/tensorizer-write-tensor-scanner-bypass/malicious_tensorizer_write_tensor_scanner_bypass.pkl" |
| ], |
| "returncode": 0, |
| "stdout": "----------- SCAN SUMMARY -----------\nScanned files: 1\nInfected files: 0\nDangerous globals: 0\n", |
| "stderr": "" |
| }, |
| "modelscan": { |
| "cmd": [ |
| "/home/hacnho/Projects/claudebounty/.claude/skills/.venv/bin/modelscan", |
| "-p", |
| "/home/hacnho/Projects/research/targets/huntr/evidence/tensorizer-write-tensor-scanner-bypass/malicious_tensorizer_write_tensor_scanner_bypass.pkl" |
| ], |
| "returncode": 0, |
| "stdout": "No settings file detected at /home/hacnho/Projects/research/modelscan-settings.toml. Using defaults. \n\nScanning /home/hacnho/Projects/research/targets/huntr/evidence/tensorizer-write-tensor-scanner-bypass/malicious_tensorizer_write_tensor_scanner_bypass.pkl using modelscan.scanners.PickleUnsafeOpScan model scan\n\n--- Summary ---\n\n No issues found! \ud83c\udf89\n", |
| "stderr": "2026-06-24 11:04:37.997158: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n2026-06-24 11:04:38.039867: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2026-06-24 11:04:39.198229: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" |
| }, |
| "load": { |
| "returncode": 0, |
| "stdout": "", |
| "stderr": "", |
| "markers": [ |
| { |
| "path": "/tmp/mfv_tensorizer_write_tensor_scanner_bypass_20260624.tensors", |
| "exists": true, |
| "size": 262357, |
| "sha256": "358202b78d25174e9c13e39e9d5437f1ad2cc01c2828ae152dc38d315c3edb03", |
| "marker_present": true |
| } |
| ], |
| "verify": { |
| "cmd": [ |
| "/home/hacnho/Projects/claudebounty/.claude/skills/.venv/bin/python3", |
| "-c", |
| "from pathlib import Path; p=Path('/tmp/mfv_tensorizer_write_tensor_scanner_bypass_20260624.tensors'); m=b'MFV_TENSORIZER_WRITE_TENSOR_SCANNER_BYPASS_20260624'; data=p.read_bytes(); print({'exists': p.exists(), 'size': len(data), 'marker_present': m in data}); raise SystemExit(0 if p.exists() and m in data else 1)" |
| ], |
| "returncode": 0, |
| "stdout": "{'exists': True, 'size': 262357, 'marker_present': True}\n", |
| "stderr": "" |
| } |
| }, |
| "gadget": { |
| "global": [ |
| "tensorizer.serialization", |
| "TensorSerializer.write_tensor" |
| ], |
| "marker": "MFV_TENSORIZER_WRITE_TENSOR_SCANNER_BYPASS_20260624", |
| "marker_paths": [ |
| "/tmp/mfv_tensorizer_write_tensor_scanner_bypass_20260624.tensors" |
| ] |
| } |
| } |
