Prompt injection and jailbreak detection over 26 languages. XLM-RoBERTa base, three independent labels, INT8 ONNX, CPU. Built for flowx-border, where it is the T2 injection detector.

Labels are multi-label rather than exclusive: direct_injection, indirect_injection, jailbreak. A single text can be more than one.

Three corpora in, the false positives on ordinary customer traffic are gone. Same architecture and hyperparameters throughout; only the corpus moved.

Measured through the shipped configuration at threshold 0.43:

v5 added one register, mundane_account_access, and it lives in the corpus generator's shared mundane set so moderation and the five single-label classifiers inherit it too. The gap it fills was invisible because the three registers already there are all prose about things in the third person, a password reset notice or an appointment booking. None of them was a customer speaking, so "How do I reset my password?" was out of distribution for every corpus anchored on them, and two detectors independently learned to treat customers as hostile.

It is not a clean sweep, and the card should say so. A bare UUID reads direct_injection at 0.944 under v5, clearing 0.43 where v4 had it at zero. It stays below 0.95. Net across both shapes v5 is ahead and the regression is real.

The previous model read a bare UUID, a git commit hash, a data URI and a sha256 digest as jailbreak or direct_injection, and read "Someone is using my account, how do I lock it?" as direct_injection at 0.98. Since the detector ships on_fail: block, that made the default policy refuse most of what a support assistant is asked. Both classes of false positive came from the same corpus property: every benign register was conversational prose, so an imperative request and a high-entropy identifier were equally out of distribution.

4,327 held-out rows, threshold 0.43, max_length 96.

Mean per-language F1 0.9891. Weakest are mt 0.8817, then ga 0.9762 and cs 0.9767. Maltese is not in XLM-RoBERTa's pretraining set, and that is a fact about the base model rather than a diagnosis: the same gap in another detector here closed entirely on corpus size alone, so read 0.8367 as a number to improve and not as a ceiling.

45,541 examples: 12,285 attacks and 33,256 benign, 27.0 percent attack share, generated with gpt-oss:120b. 26 languages evenly at 1,656 to 1,690 rows each. 19 registers, including technical_identifiers and technical_payload, and four mundane_* registers shared with the other classifiers in this family, of which mundane_account_access is v5's addition.

276 generated rows were rejected before writing: 127 placeholders, 77 duplicates, 50 in the wrong script for their language, 22 outside their length band.

A bare UUID reads direct_injection at 0.944, which clears the shipped 0.43 and not 0.95. v4 had it at zero, so this is a regression on the technical shape bought alongside a fix to the account-access one. Both are corpus properties rather than thresholds.

The account-access failure v4 could not fix is gone: "Please cancel my subscription." read direct_injection at 0.9775 under v4, identical at 0.43 and 0.95 so no threshold reached it. Two of 35,025 rows matched that phrasing then; v5 has 1,862 rows in the account-access register and 16 carrying that exact phrasing as benign.

The library ships 0.43, above this run's calibrated 0.02. The calibration deliberately is not adopted: its own report flags 0.02 as the lowest value in the sweep, which compresses scores toward zero, and macro F1 is 0.9671 even at 0.95, so the sweep is a plateau rather than a peak. A missed injection costs more than a review, so the shipped value sits well below the plateau's top without chasing the floor.

INT8 with the Gather-only quantisation recipe. 0 of 300 decisions changed against the fp32 graph, probability drift p99 0.00005, max 0.15393, and the max is one row where the p99 is what the gate reads. onnx/export_manifest.json carries the weight hashes.

Apache-2.0. Trained on synthetic data generated for this purpose.