--- library_name: pytorch tags: [prompt-injection, security, secureagentnet] --- # SecureAgentNet detector checkpoints Checkpoints for [SecureAgentNet](https://github.com/MP-GOWTHAM/SecureAgentNet). They live here rather than in git because five of the project's checkpoints are 253 MB each, against GitHub's 100 MB per-file hard limit. | Directory | What it is | Held-out AUC | |---|---|---| | `ensemble_v6_smooth3` | **Recommended detector.** From-scratch ensemble (char-CNN + BiLSTM-attention + scratch transformer, 12.3M params) on the 5-source corpus | **0.9168** | | `v3` | DistilBERT after the Track B online-retraining cycle, 66M params | 0.7875 | | `harm_detector_v3` | Content-harm classifier — a separate axis from injection | 0.8978 | | `ensemble_v9_bal` | Same architecture on the 331k 7-source corpus. Lower FPR (0.189) and higher utility (0.811) but lower AUC | 0.8929 | | `ensemble_v4_persona` | Superseded — previous ensemble on the 4-source corpus | 0.8278 | | `harm_detector` | Superseded — previous harm classifier, Necent only | 0.9028 | The recommended runtime configuration is `combined_max_v7` — the elementwise max of `ensemble_v6_smooth3` and `v3`. The two fail in opposite directions, and the combination is the only one measured with no known blind spot: 8/8 canonical short attacks and 8/8 real evasions, ASR 0.025, FNR 0.029. It is a config referencing the two members, so it is recreated locally rather than stored here. Note that `combined_max_v7` has a *lower* AUC (0.8223) than `ensemble_v6_smooth3` alone (0.9168), because taking a max inherits the union of both members' false positives. AUC is the wrong criterion for this choice — the combination exists to eliminate blind spots, not to maximise ranking. Download with `scripts/bootstrap_windows.ps1` in the repo, or manually: ```python from huggingface_hub import snapshot_download snapshot_download(repo_id="mpgowtham/secureagentnet-models", local_dir="secureagentnet/data/models") ``` See the repository's `docs/SecureAgentNet_Detector_Architecture.docx` for the full evaluation, including the configurations that failed.