Export

Export AI red teaming findings as an Apache Parquet file for data analysis, adversarial training, and compliance records. For configurable PDF and CSV reports, see Reports.

Download Parquet

Click Download Parquet from the top-right of the findings table to export the project’s findings as an Apache Parquet file.

The export is one row per trial, not one per finding: each finding expands into its individual attempts, including pruned ones. Every row carries that trial’s evidence - the adversarial prompt, the target’s response, and the judge’s reasoning - plus the complete span tree, alongside the finding metadata. The columns are grouped below.

Identity and trace

Field	Description
schema_version	Export schema version
org_key, workspace_key, project_key, project_name	Where the finding lives
assessment_id, assessment_name	The assessment the trial belongs to
trace_id, span_id	Link back to the full trace on the platform
timestamp	When the trial ran (ISO-8601 string)

Trial and finding

Field	Description
trial_index	Position of the trial within the attack
trial_status	finished, pruned, failed, …
score	Trial score (0.0 to 1.0)
judge_score	Judge/evaluator score (0.0 to 1.0)
is_jailbreak	Whether the trial met the jailbreak threshold
jailbreak_threshold	Score at/above which a trial counts as a jailbreak
severity	Per-trial severity from the trial score (lowercase: critical, high, medium, low, info)
finding_severity	Finding-level severity from the best score (may differ from the per-trial severity)
finding_type	Finding type. Generative attacks: jailbreak, partial, refusal, error. Traditional-ML (adversarial) attacks: evasion, degradation, robust, error

Attack

Field	Description
attack_name	Attack strategy (e.g. tap_attack)
goal	The attack objective
goal_category	Harm category (e.g. malware_generation)
transforms	List of transforms applied (a list<string>)

Model

Field	Description
target_model, target_model_family	Model under test
attacker_model, evaluator_model	Attacker and judge models

Evidence and usage

Field	Description
attacker_prompt, transformed_prompt	The raw and transform-applied prompt sent to the target
target_response	What the target returned
judge_reasoning	The judge’s rationale for the score
transformed_prompt_media, target_response_media	Multimodal parts as base64 data: URIs (list<string>)
input_tokens, output_tokens, total_tokens	Token usage (best-effort, may be null)
traces	The complete span tree for the run - every span (study, trial, transform, target, judge, refine, …) with its input, output, reasoning, score, timing, and span_id/parent_span_id, so multi-turn and algorithmic attacks reconstruct in full

Note

Redaction. Add the redact option in the download dialog (the endpoint accepts ?redact=true) to replace the free-form goal / attacker_prompt / transformed_prompt / target_response / judge_reasoning fields - and the same text inside serialized traces - with [REDACTED]. Use it when sharing evidence with parties who should see the metrics but not the raw attack content.

Use cases for Parquet export

• Post-safety-training improvement - because each row carries attacker_prompt + target_response, you can load successful attacks straight into an adversarial fine-tuning pipeline. Every jailbreak in the file is a training signal that directly addresses a real vulnerability.
• Risk mitigation evidence - concrete, auditable evidence of where the model fails, with the judge’s reasoning attached. This is what safety teams need to prioritize mitigations and demonstrate due diligence to compliance stakeholders.
• Custom analysis - load into Python with pandas or polars for analysis beyond what the dashboard provides:

import polars as pl

trials = pl.read_parquet("findings.parquet")

# Which transforms show up most in jailbroken trials?
trials.filter(pl.col("is_jailbreak")) \
    .explode("transforms") \
    .group_by("transforms") \
    .agg(pl.len().alias("jailbreaks")) \
    .sort("jailbreaks", descending=True)

# Which goals are most vulnerable?
trials.filter(pl.col("score") >= 0.9) \
    .group_by("goal") \
    .agg(pl.len().alias("high_score_trials")) \
    .sort("high_score_trials", descending=True)

• BI tools - import into Tableau, Looker, or Power BI for organization-wide reporting and trend tracking across model versions
• Archival - preserve a complete record of every trial for regulatory compliance and audit trails

Next steps

• Reports - configurable PDF / CSV report builder with section and filter controls (the executive-ready PDF lives here)
• Compliance - framework mapping details
• Analytics & Reporting - deep analytics charts
• Overview Dashboard - risk metrics and findings