Start with the pressure: sales, launch, abuse, agents, data, or guardrails
ARTIFACT TRIAGE
AI Artifact and Binary Security Triage Benchmark
Evaluate extension, CLI, manifest, binary, config, package, and agent artifact risk detection.
Benchmark
Extensions, CLIs, manifests, configs, Docker files, agent packages, binary metadata
Across rules-only, model-assisted, and hybrid evidence variants
Report preview
Report outputs
Publication boundary
Methodology and suite design publish before public scorecards. Suites in active build can be scoped privately while validation continues.
Problem
AI security is not only prompt security. Teams also need to inspect extensions, CLIs, agents, repos, packages, and artifacts that embed AI behavior or create supply-chain risk.
Vendors and internal teams ship AI-enabled artifacts that may request dangerous permissions, hide risky behavior, leak data, or invoke model/tool workflows without clear controls.
We will evaluate artifact triage systems against synthetic and curated files for permission risk, secret exposure, suspicious behavior, unsafe config, binary indicators, and evidence extraction quality.
Teams can triage AI-enabled artifacts, support vendor review, prioritize risky extensions or tools, and generate evidence for security review.
Benchmark scope
Scope is explicit so buyers can see what the benchmark covers before any public scorecards exist.
Classification
Target systems
Buyer problems
Risk dimensions
Evaluation task
Analyze browser extension manifests and bundles for risky permissions and AI-related data flows.
Success condition
System identifies high-risk permissions, data access, remote code, content scripts, and evidence.
Failure condition
System misses dangerous permissions or invents unsupported claims.
Evaluation task
Analyze configs, env-like files, manifests, and bundles for embedded secrets and unsafe defaults.
Success condition
System flags synthetic secrets, unsafe config, and evidence locations.
Failure condition
System misses synthetic secrets or mislabels benign config as critical.
Evaluation task
Analyze CLI tools and packaged agents for unsafe tool permissions, file/network access, and hidden behaviors.
Success condition
System identifies risky tool scope, file access, network behavior, and evidence.
Failure condition
System misses material risk or produces unsupported findings.
Evaluation task
Assess whether findings can be exported with enough proof for review workflows.
Success condition
Output includes file path, snippet, rule, rationale, severity, and remediation.
Failure condition
Output lacks proof, reproduction, or actionable remediation.
Experiment design
Hypotheses
Trial count
1,800
Repeated across prompt variants, model families, and controlled runs.
Repetitions per case
4
Enough to compare variants without pretending the scorecard is complete.
Variant
Static rules and heuristics without model-assisted review.
Captures deterministic baseline.
Variant
Model reviews extracted artifact evidence and classifies risk.
Measures triage reasoning quality.
Variant
Rules extract evidence and model summarizes risk with structured output.
Preferred commercial pathway.
Methodology
Methodology is published early so teams can understand the evaluation design, request private variants, and align internal AI security tests.
Research questions
Evaluation design
Run artifact analyzers, model-assisted reviewers, and rule-based checks across synthetic and curated artifacts with known labels. Score detection, false positives, evidence extraction, and report quality.
Sampling plan
Use synthetic browser extension manifests, JS bundles, CLI configs, Docker files, packaged agent manifests, binary metadata, and embedded secret fixtures.
Grading and statistics
Use reference labels, rule checks, permission heuristics, static indicators, rubric grading, and human review for complex findings.
Report artifact detection rate, false positive rate, evidence completeness score, and severity accuracy by artifact class.
Limitations
Version artifact fixtures, labels, extraction rules, analyzer versions, and model prompts.
Do not publish live malware, exploit kits, or usable secret material.
Metrics
Metrics are shown as reporting dimensions for the active benchmark program.
Metric
Share of risky artifacts correctly detected.
Unit
percent
Direction
higher is better
Aggregation
rate
Metric
Share of benign artifacts incorrectly flagged.
Unit
percent
Direction
lower is better
Aggregation
rate
Metric
Accuracy of severity labels for artifact findings.
Unit
score
Direction
higher is better
Aggregation
mean
Metric
Completeness of extracted proof and report-ready finding details.
Unit
score
Direction
higher is better
Aggregation
mean
Datasets
All public-safe. No raw job-description text or private corpus material is shown here.
Dataset
Synthetic extension manifests, JS bundles, CLI configs, package manifests, Docker files, binary metadata, and packaged agent fixtures.
Source
synthetic
Classification
synthetic
Item count
140
Outputs
Each output is designed to be useful without implying finished benchmark rankings.
Output
Public methodology for artifact classes, labels, extraction, scoring, and SARIF/evidence export.
Output
Private artifact risk report with evidence, findings, severity, and remediation guidance.
Status timeline
The timeline shows current build state and the publication boundary.
Status timeline
Methodology and fixtures are under active build; private scoping is available.
Status timeline
Create synthetic manifests, extension bundles, configs, and metadata fixtures.
Status timeline
Wire artifact analyzer, extraction rules, model review prompts, and SARIF export.
Commercial bridge
Private benchmark runs can be scoped now for customers, sponsors, or internal teams. Private results stay private unless explicitly approved for publication.
Private benchmark CTA
Available now
Private benchmark sprint, model comparison, product-context benchmark, and evidence bundle.
Related routes
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Claim controls
These controls keep the page safe for public use until real results exist.
Claim controls
This suite is in active build. Public artifact benchmark results will publish after validation.
Claim boundary
Do not claim