Datasets:

dangoldbj
/

a2a-metatrace

A labeled, metadata-only corpus of agent-to-agent (A2A) workflow traces, captured from official A2A sample agents composed into multi-agent workflows. Released as a standalone artifact alongside the paper; this datasheet follows the Gebru et al. "Datasheets for Datasets" structure.

Motivation

The corpus evaluates metadata leakage on workloads that are not the authors' own generator: the protocol path (Agent Cards, discovery, message/send, SSE) and the agents are officially authored; only the workflow composition and the class labels are ours.

Composition

Instances. One trace = one workflow execution. The corpus is 270 traces: 30 runs each of 9 workflow classes over 6 capabilities, each class realized by 3 compositional variants (see corpus/classes.py), runs split across a class's variants.
Per message (obs(m)). (src, dst, t, length, direction) plus ground truth: stage_idx, step_type, capability, task_class, n_stages, and a variant tag for the leave-variant-out split. Payloads are discarded: the corpus is metadata only (the capture records lengths, not bodies), so it is exactly a passive TLS observer's view and carries no message content.
Labels. task_class is the workflow class (the ground-truth the adversary recovers); variant is the specific composition (the generalization group). Labels and composition are ours; the protocol path is the real a2a-sdk.

Collection process

Each capability is an official a2a-samples agent launched as a standalone A2A server in its own env (corpus/sample_agents.py, corpus/launcher.py); an orchestrator composes them per (class, variant) and a real stage runner (corpus/runner_samples.py) records obs(m) via a real discovery round-trip + message/send + SSE streaming, the same A2A wire the measured binding observes. The runner overrides each resolved Agent Card's interface URL to the real launch port (some samples ship a stale hardcoded card URL). Transport: HTTPS-direct (the protection ladder is applied analytically in the accompanying analysis).

Honest disclosures / limitations

Provenance. The corpus is captured from official a2a-samples agents, each run unmodified as its own server: travel_planner (OpenAI), adk_currency_agent, content_planner, adk_skills_agent (Gemini via Vertex), adk_expense_reimbursement (LiteLLM→OpenAI), and helloworld (no-LLM echo). The protocol path (Agent Cards, discovery, message/send, SSE streaming) and the agent logic are official; only the workflow composition and the class labels are ours.
Model substitutions (disclosed; agent logic unchanged). Three bindings differ from the samples' shipped defaults, with no behavioral edits:
- adk_currency_agent, adk_skills_agent: model= changed gemini-3-flash-preview → gemini-2.5-flash.
- adk_expense_reimbursement: LITELLM_MODEL=openai/gpt-4o-mini (env only).
- a2a_telemetry is excluded: the sample hardcodes an ADK google_search tool with a LiteLLM model, which the current ADK rejects as-shipped. These edits are not committed into the a2a-samples clone; they are recorded here for reproducibility.
Observation model. Records are per A2A application event. Because a network observer sees TLS-record bursts, not application deltas, the accompanying analysis also reports a wire-faithful view that aggregates each stage's streamed response into one observation; recovery is robust to this (see below).
Uniformity. The sample agents are more uniform than a production deployment; a generalization limit, reported as such (leave-variant-out below).

Recommended splits

Random k-fold; leave-variant-out (generalization: hold out whole compositions, by grouping on variant so no composition spans the train/test boundary). Report both.

Known result: real, artifact-robust, composition-specific, mitigable

On this corpus (270 traces, 9 classes, chance 0.111, 8 seeds; reproduced by the accompanying analysis):

Random k-fold = 0.668 ± 0.02 (6.0× chance); leave-variant-out = 0.18 (~chance). The label-blind adversary recognizes specific, previously seen compositions, not the abstract task intent, so a held-out composition of a known class is not recovered.
Not a streaming artifact. Recovery is unchanged under the wire-faithful aggregation (per-delta 0.676 → aggregated 0.668; mean msgs/wf 376 → 11), so it rests on real workflow structure, not on the chattiest agent's per-delta volume.
Mitigable. Under a metadata-protecting transport (wire-faithful, adversary vocabulary fixed from the undefended deployment): none 6.0× → metadata-min shim 4.2× → cover 0.26× → cover+unlinkability 1.00× = exactly chance.

The corpus thus confirms the leakage claim on real official-SDK agent traffic, scopes it (recurring/profiled workflows, not zero-shot novel compositions), and shows the defense closes it.

Distribution

Dataset (Parquet) + this datasheet + the capture/generation harness (corpus/). Load with datasets / pandas (see README.md).