ARTIFACT TRIAGE
AI Artifact & Binary Triage Benchmark
AI Artifact and Binary Security Triage Benchmark
Evaluate extension, CLI, manifest, binary, config, package, and agent artifact risk detection.
Benchmark
Artifact & Binary Triage
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
Why this benchmark matters
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.
Why it matters
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.
What we will test
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.
Buyer value
Teams can triage AI-enabled artifacts, support vendor review, prioritize risky extensions or tools, and generate evidence for security review.
Benchmark scope
What this benchmark will measure
Scope is explicit so buyers can see what the benchmark covers before any public scorecards exist.
Classification
benchmark program
Target systems
Buyer problems
Risk dimensions
Evaluation task
Extension permission risk
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
Secret and config detection
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
CLI and agent package risk
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
Evidence export quality
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
Measure AI-assisted artifact triage quality across extensions, binaries, CLIs, manifests, configs, and packaged agents.
Hypotheses
- Manifest and permission analysis will be more reliable than behavioral binary triage in the MVP.
- Evidence extraction quality will be a stronger differentiator than raw detection rate.
- AI-assisted review will need rule anchors to avoid hallucinated findings.
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
Rules-only triage
Static rules and heuristics without model-assisted review.
Captures deterministic baseline.
Variant
Model-assisted triage
Model reviews extracted artifact evidence and classifies risk.
Measures triage reasoning quality.
Variant
Hybrid evidence triage
Rules extract evidence and model summarizes risk with structured output.
Preferred commercial pathway.
Methodology
How the benchmark will be run
Methodology is published early so teams can understand the evaluation design, request private variants, and align internal AI security tests.
Research questions
- How accurately can AI-assisted triage identify risky permissions, embedded secrets, and suspicious behavior?
- Can triage systems extract useful evidence from artifacts without excessive false positives?
- Which artifact classes are hardest to classify reliably?
- Can findings be exported into useful security review and SARIF-style workflows?
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
Report outputs
Metrics are shown as reporting dimensions for the active benchmark program.
Metric
Artifact detection rate
Share of risky artifacts correctly detected.
Unit
percent
Direction
higher is better
Aggregation
rate
Metric
False positive rate
Share of benign artifacts incorrectly flagged.
Unit
percent
Direction
lower is better
Aggregation
rate
Metric
Severity accuracy score
Accuracy of severity labels for artifact findings.
Unit
score
Direction
higher is better
Aggregation
mean
Metric
Evidence completeness score
Completeness of extracted proof and report-ready finding details.
Unit
score
Direction
higher is better
Aggregation
mean
Datasets
Data fixtures, source types, and public-safety boundaries
All public-safe. No raw job-description text or private corpus material is shown here.
Dataset
Synthetic artifact triage corpus v1
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
Report outputs
Each output is designed to be useful without implying finished benchmark rankings.
Output
Artifact triage methodology note
Public methodology for artifact classes, labels, extraction, scoring, and SARIF/evidence export.
Output
Private artifact triage scorecard
Private artifact risk report with evidence, findings, severity, and remediation guidance.
Status timeline
Where the suite sits now
The timeline shows current build state and the publication boundary.
Status timeline
Active build
Methodology and fixtures are under active build; private scoping is available.
Status timeline
Artifact fixture design
Create synthetic manifests, extension bundles, configs, and metadata fixtures.
Status timeline
Artifact triage harness
Wire artifact analyzer, extraction rules, model review prompts, and SARIF export.
Commercial bridge
Private benchmarking and related assets
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
Request Artifact Benchmark
Available now
Private benchmark sprint, model comparison, product-context benchmark, and evidence bundle.
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Claim controls
What the public page can and cannot say
These controls keep the page safe for public use until real results exist.
Claim controls
Public claim guardrails
This suite is in active build. Public artifact benchmark results will publish after validation.
Claim boundary
- Public scorecards are validation-gated.
- Ranking claims are not allowed.
- Vendor comparison claims are not allowed.
- This suite is in active build. Public artifact benchmark results will publish after validation.
Do not claim
- Do not claim malware detection coverage.
- Do not imply live binary reverse engineering results.
- Do not publish harmful artifacts.