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Agent Data Flow & Tool Exchange Traces

Sanitized traces of agentic AI tool calls, plugin data exchanges, and delegation chains for security research.

Status: Accepting contributions — this dataset is being built from scratch with the community.

Scope

Structured traces capturing how data flows through agentic AI systems — between agents, tools, plugins, APIs, and memory stores. This dataset supports research into DSGAI06 (Tool, Plugin & Agent Data Exchange Risks), DSGAI02 (Agent Identity & Credential Exposure), DSGAI15 (Over-Broad Context Windows & Prompt Over-Sharing), and related agent security patterns.

Categories include:

  • Tool call traces — Sequences of tool invocations showing what data is sent to and received from external tools (APIs, databases, file systems, code execution environments)
  • Multi-agent delegation chains — Traces showing how tasks and data are passed between agents, including what context is forwarded, filtered, or accumulated
  • Plugin data exchange logs — Records of data shared with third-party plugins, including request/response payloads and metadata
  • Credential and permission flows — How credentials, tokens, and permissions are scoped, delegated, and consumed across agent workflows
  • Context accumulation patterns — Traces showing how agent context windows grow over multi-step tasks, what data persists across steps, and where over-sharing occurs
  • Memory read/write traces — How agents interact with short-term and long-term memory stores, including what data is persisted and retrieved

Data Format

Contributions should include:

  • Trace ID
  • Category — From the list above
  • DSGAI mapping — Primary DSGAI entries relevant to this trace
  • Agent framework — LangGraph, AutoGPT, CrewAI, custom, etc. (if disclosable)
  • Trace data — The sequence of events, tool calls, and data exchanges in structured format (JSON, JSONL, or OpenTelemetry-compatible spans)
  • Data sensitivity annotations — Flag any steps where sensitive data is present, over-shared, or inadequately scoped
  • Security observations — What data security risks this trace illustrates
  • Benign / adversarial — Whether this is a normal workflow trace or one demonstrating a security failure

Sanitization Requirements

All traces must be sanitized before submission:

  • No real API keys, tokens, credentials, or secrets — replace with placeholder values (e.g., sk-REDACTED, Bearer EXAMPLE_TOKEN)
  • No real PII, PHI, or proprietary data — use synthetic equivalents
  • No internal hostnames, IP addresses, or infrastructure details
  • Generalize organization-specific tool names if they could identify the source

Traces from test environments, sandboxes, or CTF exercises are ideal. Production traces must be thoroughly sanitized.

Contributing

Add traces as individual JSON, JSONL, or YAML files and submit a pull request. See the main datasets README for general contribution guidelines.