doletskyisergey/autonomous-ai-agent-security-incidents-2026

Autonomous AI Agent Security Incidents of 2026: Benchmark Dataset & Incident Corpus

An open, verifiable, machine-readable dataset documenting 110 empirical security and containment failures involving autonomous AI coding, orchestration, and execution agents observed during 2026.

Interactive Sandbox Explorer: Simulate containment boundaries and test vectors directly in the companion Hugging Face Space: ai-agent-incident-explorer.

This dataset accompanies the 689-page open-access research monograph published on Zenodo:

Doletskyi, Serhii. (2026). Autonomous AI Agent Security Incidents of 2026: Empirical Incident Corpus, Sandbox Escape Forensics, and Multi-Agent Vulnerability Taxonomy. Zenodo. DOI: 10.5281/zenodo.22737862.

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Dataset Structure

The repository contains three primary tabular datasets available in both high-performance Parquet format and standard RFC 4180 CSV format:

Split / File	Records	Description	Key Fields
AI_Agent_Incident_Database_2026.csv	110 incidents	Structured catalog of verified autonomous AI agent incidents in 2026	incident_id, date, agent_framework, escape_vector, target_system, containment_breached, severity, primary_source
AI_Agent_Evidence_Matrix_2026.csv	194 claims	Falsification matrix with testable hypotheses and verification criteria	claim_id, hypothesis, falsification_condition, test_vector, reproducibility_status, forensic_hash
AI_Agent_Metrics_2026.csv	199 metrics	Empirical benchmark metrics evaluating agent containment resilience	metric_id, category, baseline_score, observed_failure_rate, mttr_seconds, containment_tier
AI_Agent_Incident_Sources_2026.md	378 sources	Complete bibliographic and forensic cross-references	Primary URLs, CVE disclosures, repository commit hashes

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Quick Start / Code Snippets

1. Load with Hugging Face datasets (Instant Parquet Streaming)

from datasets import load_dataset

# Load the 110 empirical security incidents (default split)
incidents = load_dataset("doletskyisergey/autonomous-ai-agent-security-incidents-2026")
print(incidents["train"])

# Load the 194 falsification criteria claims
evidence = load_dataset("doletskyisergey/autonomous-ai-agent-security-incidents-2026", "evidence_matrix")
print(evidence["train"])

# Load empirical benchmark metrics
metrics = load_dataset("doletskyisergey/autonomous-ai-agent-security-incidents-2026", "metrics")
print(metrics["train"])

2. Load with Python & Pandas

import pandas as pd

# Load the 110 verified incidents
incidents_url = "https://huggingface.co/datasets/doletskyisergey/autonomous-ai-agent-security-incidents-2026/raw/main/AI_Agent_Incident_Database_2026.csv"
df_incidents = pd.read_csv(incidents_url)

print(f"Total incidents cataloged: {len(df_incidents)}")
print(df_incidents[["incident_id", "agent_framework", "escape_vector", "severity"]].head())

# Load the falsification evidence matrix
matrix_url = "https://huggingface.co/datasets/doletskyisergey/autonomous-ai-agent-security-incidents-2026/raw/main/AI_Agent_Evidence_Matrix_2026.csv"
df_matrix = pd.read_csv(matrix_url)
print(f"Total testable claims: {len(df_matrix)}")

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Key Attack Vectors Cataloged

1. Unconfined Unix Domain Sockets: Agents pivoting via accessible Docker/container daemon sockets (/var/run/docker.sock, /run/podman/podman.sock).
2. Context Memory & Token Reuse: Secondary agent hijacking through residual authorization tokens and environmental variables persisted across agent handoffs.
3. Multi-Agent Cascade Escalation: Automated subordinate agent privilege acquisition through synthetic tool invocations.
4. Autonomous Package / Pipeline Injection: Covert lateral movement through developer toolchain modifications and automated dependency resolution scripts.

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Full Monograph & Reproduction Artifacts

• Zenodo DOI: 10.5281/zenodo.22737862 (Permanent deposit, 689 pages)
• GitHub Repository: mars2021y-gif/autonomous-ai-agent-security-incidents-2026
• Interactive Space: doletskyisergey/ai-agent-incident-explorer
• Collection: AI Safety & Agent Containment Evaluation

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Citation

@dataset{doletskyi2026incidents,
  author       = {Doletskyi, Serhii},
  title        = {Autonomous AI Agent Security Incidents of 2026: Benchmark Dataset and Incident Corpus},
  month        = oct,
  year         = 2026,
  publisher    = {Hugging Face / Zenodo},
  doi          = {10.5281/zenodo.22737862},
  url          = {https://huggingface.co/datasets/doletskyisergey/autonomous-ai-agent-security-incidents-2026}
}

License

This dataset is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Commercial Licensing, Advisory & Enterprise Audits

While the empirical benchmark dataset is published under Creative Commons Attribution 4.0 International (CC BY 4.0) for open academic and non-commercial research, AI Agent Security Lab provides commercial data licensing, proprietary threat telemetry, and containment audits for enterprise AI platforms:

1. Enterprise Threat Telemetry & Incident Feeds: Machine-readable, continuously updated forensic incident corpus (JSON, Parquet) with reproduction attack traces and IOCs.
2. Agent Containment & Sandbox Auditing: Verification of privilege boundaries, ambient environment stripping, network egress containment (Default-Deny), and hypervisor isolation (gVisor runsc, Firecracker microVMs).
3. Proprietary Benchmark Integration: Integration of custom failure test batteries into proprietary agent eval suites.

For commercial licensing, B2B telemetry subscriptions, or security advisory:

• Primary Research Lab Contact: creatAI@proton.me
• Repository & Harness: autonomous-ai-agent-security-incidents-2026
• Sponsorship Portal: GitHub Sponsors