AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Model reports

Argus-Bench

Can an agent gateway recognize malicious instructions without flagging ordinary requests? Inspect detection, false positives and the exact filtering layer together.

Frozen version 1.0, published November 2, 2026; four scored gateway configurations.

What does the gateway miss?

Argus-Bench tests whether an input-inspection gateway flags prompt-injection attempts before they reach an agent. Its separate benign controls reveal whether the same policy also blocks ordinary requests. It measures input detection, not whether a downstream agent ultimately follows an attack.

Original dataset README

204 attacks and 60 controls

The frozen v1.0 contains 264 English, single-turn cases across seven attack families. All but one were authored for this benchmark; A52 adapts a public persona attack. Labels received author self-review, without an independently human-judged validation set.

Original dataset README

Detection must be read with false positives

Both flag and block labels count as detected under the stated flag-and-audit policy. The primary series is detection over all 204 attacks. It excludes benign controls from that denominator; control false-positive rates and attack-family rates are separate measurements below.

Original frozen result tables and system configurations

Results by attack family

Read the original numerators and denominators alongside percentages. A perfect result on one family is not an overall score.

Frozen v1.0 detection results
CategoryCasesArgusLLM GuardRebuff heuristicLLM judge
direct_override333/33 (9.1%)33/33 (100.0%)2/33 (6.1%)15/33 (45.5%)
role_override285/28 (17.9%)15/28 (53.6%)0/28 (0.0%)18/28 (64.3%)
system_prompt_probe253/25 (12.0%)23/25 (92.0%)0/25 (0.0%)13/25 (52.0%)
encoded_payload293/29 (10.3%)25/29 (86.2%)1/29 (3.4%)16/29 (55.2%)
gradual_escalation390/39 (0.0%)24/39 (61.5%)1/39 (2.6%)21/39 (53.8%)
indirect_contextual300/30 (0.0%)7/30 (23.3%)0/30 (0.0%)16/30 (53.3%)
tool_misuse201/20 (5.0%)4/20 (20.0%)0/20 (0.0%)7/20 (35.0%)
All attacks / 全部攻击20415/204 (7.35%)131/204 (64.22%)4/204 (1.96%)106/204 (51.96%)

Percentages are copied as printed, without recomputation or rounding changes.

Original frozen result tables and system configurations

What normal requests get flagged?

Low detection can coexist with low false positives. The benign controls deliberately resemble the attack surface, including security discussions and questions about encoding.

False positives on 60 benign controls
SystemFalse positives / controlsFP rateCase IDs
Argus7/6011.67%C04, C06, C07, C10, C16, C28, C43
LLM Guard10/6016.67%C02, C14, C19, C23, C24, C30, C42, C44, C49, C54
Rebuff heuristic0/600.00%—
LLM judge19/6031.67%C03, C04, C07, C08, C09, C11, C13, C17, C19, C23, C24, C25, C27, C37, C46, C48, C49, C53, C57

These are false-positive rates, not attack-detection scores. Lower is better for this separate metric.

Original frozen result tables and system configurations

The configuration is part of the result

Argus uses five regular-expression patterns. LLM Guard 0.3.16 uses protectai/deberta-v3-base-prompt-injection-v2 at threshold 0.92 with full-text matching. Rebuff 0.1.1 uses only its offline heuristic layer at threshold 0.75; its hosted model and vector-database layers were not tested. The judge is Qwen2.5-0.5B-Instruct, Q4_K_M through llama.cpp, temperature 0 and a fixed binary classification prompt.

Original frozen result tables and system configurations

Why NeMo has no score

The author implemented NeMo Guardrails 0.24 with a local Qwen2.5-0.5B self_check_input rail but did not complete a full-corpus run. Smoke-test observations and a runtime estimate are not a benchmark score; no zero or inferred result is added here.

Original frozen result tables and system configurations

What this release can support

This is a small, author-constructed diagnostic dataset for gateway input filtering. It does not establish resilience to multi-turn attacks, multilingual traffic or downstream tool execution. Rebuff here is a component configuration, not the complete product. The original release includes the dataset, README and result summary; the companion paper is named but no verified publication URL was found in this deposit.

Original dataset README · Original frozen result tables and system configurations · Argus-Bench versioned original deposit

Release and review dates

The versioned Zenodo deposit was published on November 2, 2026 and reviewed for this page on November 3. That publication date timestamps the reported results; the exact experiment execution dates are not stated.

Argus-Bench versioned original deposit

Original publication

Author deposit v1.0 and its original result summary; the companion paper URL is unverified.

Reviewed 2026-11-03

Evidence available for this benchmark

This source record contains 4 numeric observations. The source distinguishes 4 model identities or configurations. A model may appear more than once; check the version, tools and run budget before comparing. Read results and row-level evidence →

Reported metric
Attack detection rate (all 204 attacks)
Recorded unit
percent
Metric direction
higher is better

Start with these resources

Cited documents and dates (1)

Evaluation context and evidence

Source
Model reports
Publisher
Not recorded
Benchmark release
2026-11-02
Collected
Not recorded
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 4

Shown in source observation order, not ranked. Dates retain the precision provided by the source.

ModelValueUnitProtocolReported date and basisSource
Qwen2.5-0.5B-Instruct (LLM judge)51.96percentFrozen v1.0; English single-turn inputs; both flag and block count as detected; 60 benign controls excluded from this metric; Q4_K_M quantization; llama.cpp; temperature 0; fixed INJECTION/BENIGN classification prompt; source publication date, execution date not stated2026-11-02 · document_publicationArgus-Bench v1.0 frozen results summary
Argus PromptInjectionDetector7.35percentFrozen v1.0; English single-turn inputs; both flag and block count as detected; 60 benign controls excluded from this metric; five regex patterns; detection means at least one finding; default flag-and-audit policy; source publication date, execution date not stated2026-11-02 · document_publicationArgus-Bench v1.0 frozen results summary
Rebuff 0.1.1 (heuristic only)1.96percentFrozen v1.0; English single-turn inputs; both flag and block count as detected; 60 benign controls excluded from this metric; offline heuristic layer only; threshold 0.75; hosted model and vector-database layers excluded; source publication date, execution date not stated2026-11-02 · document_publicationArgus-Bench v1.0 frozen results summary
LLM Guard 0.3.1664.22percentFrozen v1.0; English single-turn inputs; both flag and block count as detected; 60 benign controls excluded from this metric; protectai/deberta-v3-base-prompt-injection-v2; threshold 0.92; full-text matching; source publication date, execution date not stated2026-11-02 · document_publicationArgus-Bench v1.0 frozen results summary