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Datasets:
alpha-one-index
/
ai-appsec-index

Tasks:
Text Classification
Tabular Classification
Languages:
English
Size:
n<1K
Tags:
application-security
appsec
ai-security
vulnerability
cve
aspm
License:
Dataset card Files
xet
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
metadata: struct<title: string, description: string, version: string, last_updated: timestamp[s], methodology: string, total_cve_mappings: int64, cross_references: list<item: string>>
ai_tool_vulnerabilities: list<item: struct<cve_id: string, tool: string, severity: string, cvss: double, type: string, description: string, cwe_id: string, disclosure_date: timestamp[s], source_url: string, status: string>>
vulnerability_categories: list<item: struct<cwe_id: string, cwe_name: string, ai_prevalence: string, ai_prevalence_score: int64, description: string, affected_generators: list<item: string>, real_cve_examples: list<item: string>, research_source: string, source_url: string, mitigation: string, example_languages: list<item: string>>>
aggregate_statistics: struct<total_cwes_tracked: int64, total_cve_mappings: int64, ai_tool_cves: int64, high_prevalence_count: int64, medium_prevalence_count: int64, critical_prevalence_count: int64, most_affected_generator: string, most_common_language: string, average_prevalence_score: double, data_sources_count: int64, note: string>
vs
metadata: struct<title: string, description: string, version: string, last_updated: timestamp[s], methodology: string, framework: string, cross_references: list<item: string>>
capability_definitions: list<item: struct<id: string, name: string, description: string>>
vendors: list<item: struct<vendor: string, product: string, funding_total: string, valuation: string, headquarters: string, founded: int64, capabilities: struct<asset_discovery: int64, vulnerability_correlation: int64, risk_prioritization: int64, remediation_orchestration: int64, policy_enforcement: int64, compliance_mapping: int64, sbom_management: int64, ai_code_detection: int64>, overall_score: double, gartner_recognition: string, source_url: string, source_date: timestamp[s], notes: string>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              metadata: struct<title: string, description: string, version: string, last_updated: timestamp[s], methodology: string, total_cve_mappings: int64, cross_references: list<item: string>>
              ai_tool_vulnerabilities: list<item: struct<cve_id: string, tool: string, severity: string, cvss: double, type: string, description: string, cwe_id: string, disclosure_date: timestamp[s], source_url: string, status: string>>
              vulnerability_categories: list<item: struct<cwe_id: string, cwe_name: string, ai_prevalence: string, ai_prevalence_score: int64, description: string, affected_generators: list<item: string>, real_cve_examples: list<item: string>, research_source: string, source_url: string, mitigation: string, example_languages: list<item: string>>>
              aggregate_statistics: struct<total_cwes_tracked: int64, total_cve_mappings: int64, ai_tool_cves: int64, high_prevalence_count: int64, medium_prevalence_count: int64, critical_prevalence_count: int64, most_affected_generator: string, most_common_language: string, average_prevalence_score: double, data_sources_count: int64, note: string>
              vs
              metadata: struct<title: string, description: string, version: string, last_updated: timestamp[s], methodology: string, framework: string, cross_references: list<item: string>>
              capability_definitions: list<item: struct<id: string, name: string, description: string>>
              vendors: list<item: struct<vendor: string, product: string, funding_total: string, valuation: string, headquarters: string, founded: int64, capabilities: struct<asset_discovery: int64, vulnerability_correlation: int64, risk_prioritization: int64, remediation_orchestration: int64, policy_enforcement: int64, compliance_mapping: int64, sbom_management: int64, ai_code_detection: int64>, overall_score: double, gartner_recognition: string, source_url: string, source_date: timestamp[s], notes: string>>

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AI AppSec Index

The definitive open-source reference for AI-augmented application security.

Dataset Description

6 structured datasets covering the AI AppSec landscape:

Dataset Records Description
AI Remediation Leaderboard 9+ Benchmark scores for AI-powered code remediation engines
ASPM Capability Matrix 15+ Vendor comparison across 20+ capabilities
AI-Code Vulnerability Tracker 48+ Real CVEs in AI-generated code mapped to CWEs
EU CRA Compliance Map 12+ Tool-by-tool CRA regulatory readiness
False Positive Benchmark 20+ FP rates by tool, language, and CWE
CRA Timeline 7 Key compliance dates and requirements

Usage 

from datasets import load_dataset
ds = load_dataset("alpha-one-index/ai-appsec-index")

Or install the Python package:

pip install git+https://github.com/alpha-one-index/ai-appsec-index.git
import ai_appsec_index
data = ai_appsec_index.load_dataset("remediation-leaderboard")

Links

Citation 

@dataset{ai_appsec_index_2026,
  title={AI AppSec Index},
  author={Alpha One Index},
  year={2026},
  url={https://github.com/alpha-one-index/ai-appsec-index}
}
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