> Source: https://border-landing.vercel.app/docs/reference/performance
> Full documentation index: https://border-landing.vercel.app/llms.txt
> This file is generated from the flowx-ai/border repository.


Generated from the library's own benchmark run.

Every score below carries the number of examples behind it, and the `n means`
column says what those examples are. A count of positives is not a count
of everything evaluated, and the two are not interchangeable. Where the
evaluation recorded no count at all the cell reads `not recorded`, which
means the score is unverified rather than good or bad.

## Detectors

| Detector | Tier | Status | Metric | Macro | Median | Worst | Test cases | n means |
|---|---|---|---|---|---|---|---|---|
| `banned_terms` | T1 | built | – | – | – | – | – | – |
| `bias` | T2 | built | f1 | 0.977 | 1.000 | 0.824 | 264 | positive examples |
| `code_present` | T1 | built | – | – | – | – | – | – |
| `disclosure` | T0 | built | – | – | – | – | – | – |
| `encoded_payload` | T1 | built | – | – | – | – | – | – |
| `gibberish` | T1 | built | f1 | 0.966 | 0.958 | 0.870 | 276 | positive examples |
| `groundedness` | T3 | not built | – | – | – | – | – | – |
| `injection` | T2 | built | f1 | 0.970 | 0.983 | 0.727 | 357 | positive examples |
| `internal_domains` | T1 | built | – | – | – | – | – | – |
| `invisible_text` | T0 | built | – | – | – | – | – | – |
| `json_schema` | T1 | built | – | – | – | – | – | – |
| `language_id` | T1 | built | – | – | – | – | – | – |
| `markup_injection` | T1 | built | – | – | – | – | – | – |
| `moderation` | T2 | not built | – | – | – | – | – | – |
| `nsfw` | T2 | built | f1 | 0.934 | 0.952 | 0.600 | 259 | positive examples |
| `output_format` | T1 | built | – | – | – | – | – | – |
| `output_leakage` | T1 | built | – | – | – | – | – | – |
| `pii` | T1 | built | – | – | – | – | – | – |
| `politeness` | T2 | built | f1 | 0.962 | 0.968 | 0.788 | 392 | positive examples |
| `postal_code` | T1 | built | – | – | – | – | – | – |
| `regulated_advice` | T2 | built | f1 | 0.995 | 1.000 | 0.957 | 622 | positive examples |
| `repetition` | T1 | built | – | – | – | – | – | – |
| `secrets` | T0 | built | – | – | – | – | – | – |
| `sql_injection` | T1 | built | – | – | – | – | – | – |
| `summary_support` | T1 | built | – | – | – | – | – | – |
| `system_prompt_leakage` | T1 | built | – | – | – | – | – | – |
| `token_limit` | T1 | built | – | – | – | – | – | – |
| `topic_scope` | T3 | built | top1_accuracy | 0.865 | 0.857 | 0.375 | 175 | examples evaluated |
| `toxicity` | T2 | built | f1 | 0.992 | 1.000 | 0.950 | 518 | positive examples |
| `url_reachability` | T3 | built | – | – | – | – | – | – |

## Caveats

- **`bias`**: 12 of 26 languages have fewer than 10 positive examples: az, bg, da, de, el, fi, ga, hu, lv, mt, pl, sk. Their individual scores are indicative rather than measured.
- **`gibberish`**: 2 of 26 languages have fewer than 10 positive examples: bg, en. Their individual scores are indicative rather than measured.
- **`nsfw`**: 1 of 26 languages have fewer than 10 positive examples: ga. Their individual scores are indicative rather than measured.
- **`topic_scope`**: 26 of 26 languages have fewer than 10 examples evaluated: az, bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv, tr. Their individual scores are indicative rather than measured.
- **`topic_scope`**: no calibrated threshold recorded, so this detector runs at the policy default. Several detectors in this family reported nothing at 0.5 while separating positives from negatives well below it.

## Latency

At a 396 character reference input, 1 thread, CPUExecutionProvider. Romanian prose with no entities in it, so this measures the cost of looking rather than the cost of finding.

| Detector | p95 ms | Budget ms | note |
|---|---|---|---|
| `banned_terms` | 0.168 | 5.0 | – |
| `bias` | 21.327 | 225.0 | – |
| `code_present` | 0.009 | 5.0 | – |
| `disclosure` | 0.038 | 5.0 | – |
| `encoded_payload` | 0.205 | 5.0 | the unconfigured path |
| `gibberish` | 27.528 | 225.0 | – |
| `injection` | 27.622 | 225.0 | – |
| `internal_domains` | 0.234 | 5.0 | – |
| `invisible_text` | 0.038 | 5.0 | – |
| `json_schema` | 0.001 | 5.0 | – |
| `language_id` | 0.362 | 5.0 | – |
| `markup_injection` | 0.239 | 5.0 | – |
| `nsfw` | 27.544 | 225.0 | – |
| `output_format` | 0.001 | 5.0 | – |
| `output_leakage` | 27.991 | 225.0 | – |
| `pii` | 27.636 | 225.0 | – |
| `politeness` | 30.319 | 225.0 | – |
| `postal_code` | 0.002 | 5.0 | – |
| `regulated_advice` | 30.430 | 225.0 | – |
| `repetition` | 0.497 | 5.0 | – |
| `secrets` | 0.048 | 1.0 | – |
| `sql_injection` | 0.238 | 5.0 | – |
| `summary_support` | 0.921 | 5.0 | – |
| `system_prompt_leakage` | 0.318 | 5.0 | the unconfigured path |
| `token_limit` | 0.001 | 5.0 | the unconfigured path |
| `topic_scope` | 46.298 | 300.0 | – |
| `toxicity` | 27.224 | 225.0 | – |
| `url_reachability` | 0.007 | 3000.0 | the unconfigured path |