Unforge Science Margin

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Margin Measuring guardrail robustness

Margin measures how prompt changes impact guardrail errors, including unsafe requests slipping through while the protected model still understands them.

Public benchmarks provide useful baselines, but fixed examples can reward tuning to the test and may not reflect your domain or policy. High accuracy on those prompts does not show how decisions hold up as requests change. Margin complements these benchmarks by measuring robustness across prompt variations and testing guardrails together with the models they protect.

Guardrail robustness leaderboard

API access

This candidate leaderboard measures how much transformation it takes to make a correct guardrail decision fail. It uses the cost of the first observed error for each prompt, measured against XSTest’s ground-truth labels. Overall robustness gives equal weight to the unsafe and safe prompt classes.

Higher is better in every column. Robustness scores (0–100) measure resistance to letting unsafe prompts through (false allows) or blocking safe prompts (false blocks). Overall robustness averages these two scores. Original prompt accuracy (%) measures correct decisions before transformations.

Open a checkpoint’s evidence for robust-accuracy curves and original-prompt performance. Scroll sideways to compare all metrics.

Checkpoints ranked by overall robustness on XSTest. Robustness scores are unitless and range from zero to one hundred; higher is better. Overall robustness gives equal weight to unsafe and safe prompts. Original prompt accuracy is class-balanced accuracy on original prompts, expressed as a percentage.
RankCheckpoint
1 AI2WildGuard 47.27 7.3087.2594.65%
2 GoogleShieldGemma 44.00 5.3782.6479.05%
3 IBMGranite Guardian 39.79 7.6771.9287.90%
4 AlibabaQwen3Guard 38.89 7.8969.8891.85%
5 OpenAIgpt-oss-safeguard-20b 36.84 7.5066.1791.50%
6 MistralShieldstral 34.74 7.6761.8092.70%
7 NVIDIANemotron Safety Guard 27.58 7.5647.6085.75%
8 TypeSafeJev 26.92 6.8746.9786.40%
9 MetaLlama Guard 4 10.02 9.1810.8785.20%

Remark. Most guardrails in this benchmark are much less robust against false allows: scores are about 5–9/100, versus 47–87/100 for false blocks in eight of the nine. Llama Guard 4 scores poorly on both. These guards correctly detect 66.5–96.5% of original unsafe prompts; their weakness is maintaining those correct decisions as prompts change.

Missing a guardrail? Suggest one

API access

Get the complete guardrail leaderboard as JSON. Access is free and no API key is required.

GET /api/v1/margin/guardrails

GET /api/v1/margin/guardrails
meta
Snapshot date, protocol, metric units, methodology, and evaluation provenance. Follow meta.snapshot_url to pin an immutable version.
data
One record per guardrail, ordered by overall robustness.
id · rank
Stable guardrail ID and position in the default overall ranking.
balanced_margin
Overall robustness, from 0 to 100.
unsafe_margin · safe_margin
False allow robustness and false block robustness, from 0 to 100.
clean_balanced_accuracy
Class-balanced original prompt accuracy, as a percentage.
evaluation · curves
Checkpoint and policy details, any provisional-result caveats, and robust-accuracy curves. Curve accuracy is a fraction from 0 to 1.

Results update when new evaluations are published. Fetch once and filter or sort locally. Open JSON. When citing these results, credit Unforge Science and link to Margin.

How is margin calculated?

For each prompt, we find the cheapest tested transformation that makes the verdict disagree with its XSTest label. A completed output without a classification counts as an error. An incorrect original verdict contributes zero. If no error is observed, we cap the cost at the largest tested budget. We average these costs separately for the 200 unsafe and 250 safe prompts, normalize each class margin to 0–100, then take their arithmetic mean.

Cost is Unicode character insertions, deletions, and substitutions, including prompt wrappers, per 100 original characters across the corpus. Added instructions can make the cost exceed 100. Every guard uses the same transformations, cost range, and uniform weighting across budgets. The ranking is calculated from guard verdicts alone.

This also equals the normalized area under robust accuracy as the budget grows. A prompt counts as correct only while its original and every tested transformation within the budget agree with ground truth. Baseline errors stay at zero even if a later transformation corrects them. Safe and unsafe refer to XSTest’s labels; labels are inherited under the selected transformations’ meaning-preservation assumption.

No half-flip cutoff is required. Margins summarize the finite tested suite; a capped cost establishes that no error was found within that suite. Original prompt accuracy is the equally weighted mean of accuracy on the original unsafe and safe prompts. Both the margin average and clean accuracy allow one class to compensate for the other.

Compare scores within the same protocol and transformation suite. A changed suite or cost range requires a new comparison. The snapshot download includes the observed flip survival curves, baseline accuracy, and evaluation provenance.

What changes when a model sits behind the guard?

API access

This paired evaluation tests whether a prompt transformation can make a guard allow a previously blocked request while preserving the protected model’s behavior. It checks the guard’s verdict and the model’s response for the same transformed prompts.

Each cell is a guard–model pair. Rows are guardrails; columns are protected models ordered by Intelligence Index.

  • Bypass found: A transformation passed the guard while preserving model behavior.
  • None found: No qualifying bypass was observed in the tested search.
Whether a bypass is known for each pairing of guard checkpoint and target checkpoint. Columns ascend by Intelligence Index; checkpoints level on the index are ordered by weight size. Rows are guard checkpoints, newest release first. Release dates for open-weight guards are model repository creation dates, read from Hugging Face on 10 September 2026. Jev has no model repository to read a date from: its date is the release date the catalogue of the service that sells it states, read on 20 September 2026.
Guardrails (rows); target models (columns) Qwen3.5 0.8BIndex 6 Qwen3.5 2BIndex 7 Gemma 4 E2BIndex 8 Granite 4.2 3BIndex 9 Gemma 4 E4BIndex 9 Ling 3.0 TinyIndex 12 Granite 4.2 8BIndex 12 Qwen3.5 4BIndex 13 Qwen3.5 9BIndex 14 Gemma 4 12BIndex 14 Granite 4.2 30BIndex 15 Gemma 4 31BIndex 15 Gemma 4 26B A4BIndex 17 Muse Glimmer (high)Index 18 Qwen3.5 27BIndex 23
Jev no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
Shieldstral no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
Granite Guardian no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
Qwen3Guard no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
gpt-oss-safeguard-20b no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
Nemotron Safety Guard no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
Llama Guard 4 no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
ShieldGemma no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known
WildGuard no bypass known no bypass known no bypass known no bypass known no bypass known no bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known bypass known

All nine guardrails were bypassed with every tested model scoring 13 or higher on the Intelligence Index, allowing transformed requests through while preserving the protected models’ behavior.

Missing a target model? Suggest one

API access

Get the complete guard–model pairing results as JSON. Access is free and no API key is required.

GET /api/v1/margin/pairings

GET /api/v1/margin/pairings
meta
Paired-study snapshot date, protocol, judge, thresholds, and methodology. Follow meta.snapshot_url to pin an immutable version.
meta.guards · meta.models
Names, creators, and stable IDs. Model metadata also describes the tested search.
data
One record per tested guard–model pair.
guard_id · model_id
IDs linking each result to the guard and model metadata.
status
bypass_found or no_bypass_found. None found means none qualified within the tested search; it does not establish safety. Untested pairs are omitted.
qualifying_transformations
Number of transformations that met the paired study’s evasion, regression, and behavior-preservation criteria.
transformations_tested
Number of transformations searched for the protected model. Search breadth varies by model.

This endpoint retains the paired study’s evidence and policies, independently of later guardrail-only reruns. It includes Margin’s measurements and source links for model context. Open JSON. When citing these results, credit Unforge Science and link to Margin.

Paired evaluation criteria and scope

The v0.8 study requires a transformation to flip at least 97% of the guard’s originally unsafe verdicts to safe, flip no more than 2% of originally safe verdicts to unsafe, and achieve a judged model-behavior preservation rate of at least 50%. These criteria define “bypass found” in the paired study. They do not enter the balanced margin calculation above.

The paired study retains its 29 September 2026 evaluation evidence and policies; the two checkpoint reruns update the guard-only leaderboard. Most protected models were tested with two transformations, with broader searches for some models. The 28 transformations in the leaderboard were not all evaluated against every protected model.

Capability ladders visualization

All nine guards had a qualifying bypass at Qwen 3.5 4B, Granite 4.2 8B, and Gemma 4 12B. The figure relates these observations to model capability and weight size. Capability alone does not predict a bypass.

Capability ladders

Each point is a tested model. Hover or focus a point for its name. Weight size uses a logarithmic scale. Green shading follows models with no bypass found; red follows full coverage. Shading summarizes the measured points, not a safety boundary.

How Margin measures the gap

Start with a prompt domain and a guardrail. Measure how its decisions change across transformations and their costs. Add a protected model to test whether its generation behavior is preserved. The illustration follows the unsafe→safe direction; the leaderboard measures both directions.

A transformed prompt in the robustness gap The original prompt p is blocked by the guard. A transformation moves it outside the illustrated blocking region while remaining within a region where the model’s generation behavior is roughly preserved. A qualifying transformation must receive a safe verdict from the guard and retain target-response utility. Failure to understand alone does not establish a safe verdict. These regions are conceptual, not measured boundaries. Model behavior preserved Guard still blocks
Transformation TT
pp
T(p)T(p)

A qualifying bypass

Guard allows it.
Model responds similarly.

Changing how a request is written can change the guard’s verdict without changing the model’s response. Margin tests both effects for the same transformation.