Research with operational controls
Queue hunts, assign work to workers, set budgets, and track usage and cost. Pause or steer managed jobs at checkpoints, with commands and acknowledgements recorded.
Find the fault.
Keep the proof.
Turn open-ended vulnerability research into a traceable process. Run focused hunts, inspect every decision, and keep the evidence that matters.
For researchers, security teams, and the agents they work with.
fractureai / Research runsHUNT-0042 · WORDPRESS PLUGIN · PHP
Who can export private records?
Example Forms 2.4.1 · Permission boundary review
- Token usage
- 0
- 0 input · 0 output
- Estimated cost
- €0.00 / €10.00
- 0.0% of hunt budget
- Hypotheses
- 5
- 0 not reproduced
- Tests completed
- 0
- 00:00 elapsed
Hypothesis board
- H-01Export action skips a permission checkQueued
- H-02Direct entry route exposes another owner’s recordsQueued
- H-03Anonymous request retrieves private export dataQueued
- H-04Expired REST nonce is acceptedQueued
- H-05Alternate route bypasses the same guardQueued
Negative results apply only to the tested routes, accounts and inputs.
Research activityPaused
- ControllerResearch objective
The fixture contains private form entries. Only administrators may export them; individual entries are readable by their owner or an administrator. Test the five listed hypotheses.
Interactive hunt preview · Playback condensed to show the workflow.
Inside the hunter
Every result shapes the next question.
Each hunt develops and tests hypotheses as the investigation progresses. These example branches show how results shape the next experiment and which candidates proceed to reproduction and review.
TARGET CONTEXTCode · scope · research objective
Fracture HunterHypotheses · experiments · feedback
Example research paths
H-01 Export accessIsolated test Candidate
H-02 Direct entry accessIsolated test Not reproduced
H-03 Anonymous accessIsolated test Not reproduced
H-04 Expired nonceIsolated test Not reproduced
H-05 Alternate routeIsolated test Not reproduced
COMPARE THE EVIDENCERetain every outcome · refine the next test
Feedback to the hunter · refine and repeat
SUPPORTED CANDIDATE
REPRODUCE & REVIEWA finding you can inspect.
Beyond the model call
Built to run the investigation.
fractureai goes beyond an API wrapper or a prompt-and-tool loop. It manages the work around the model: durable research jobs, isolated test environments, execution controls, usage tracking, and evidence that remains available after the conversation ends.
The model helps decide what to investigate. fractureai gives that investigation a place to run, a record of what happened, and a path to review the result.
Evidence you can return to
Search run events, retrieve full tool transcripts, and inspect the original artifacts. Checksums connect the evidence you review to the bytes that were stored.
Built for agent-led operations
Use the dashboard or let Codex and OpenCode work through explicit APIs and MCP tools. Jobs, controls and research records live in the platform, so the workflow can continue across sessions.
fractureai · Infrastructure announcementBehind the hunt
fractureai
Built on AMD GPUs
The compute behind the research.
Our backend runs on AMD GPUs. fractureai brings hunt orchestration, job controls and a complete evidence trail to the research running on that infrastructure.
An update from fractureai about the hardware we use. No AMD partnership or endorsement is implied. AMD and the AMD Arrow logo are trademarks of Advanced Micro Devices, Inc.
fractureai · Model integrationPrimary hunter
Primary hunter models from Z.ai.
fractureai uses Z.ai's GLM models to power its primary hunter. They help reason about code, develop hypotheses and guide tests, while fractureai preserves the tool calls, transcripts and evidence for review.
Explore Z.ai models ↗
Z.aiWhere our models run.
We run models entirely on our own infrastructure in the Karlcom.de data center. We also use Z.ai models hosted in German data centers for our hunts.
Z.ai is credited as the provider of the models used in this integration. fractureai is independent; no partnership, sponsorship or endorsement is claimed. Z.ai's name and logo belong to their respective owner.
The trace is the asset
Every conclusion needs a trail.
Search across runs. Read the complete transcript. Retrieve the exact artifact behind a finding. fractureai keeps the details available when the interesting question is “why?”
- Searchable operational events
- Original logs and immutable evidence
- Checksums and explicit import exceptions
- Human and agent review in the same workflow
Example evidence bundle
Verification attemptsJSON
Tool transcriptFull trace
Reproduction scriptArtifact
Source snapshotSHA-256
Original bytes remain available. A summary never replaces the source.
Bring your own agent
One workflow. Your preferred interface.
Work in the browser, Codex, or OpenCode. The same durable jobs and explicit controls sit underneath.
Ask a focused question
“Show the failed parser tests from the last run, retrieve their evidence, and explain what changed.”
Bounded search results keep context useful. Full event payloads and artifacts are one step away.
Make the next action explicit
fracture_control({"id": "<managed-job-id>", "action": "pause", "idempotency_key": "review-parser-run"})Example request. A recorded command is distinct from its acknowledgement.
Language-aware today. Tool-extensible by design. Ghidra headless integration is planned; it is not presented as an available analyzer yet.
Fair questions
What you should know.
What the platform does, what the model contributes, and where human judgment still matters.
Why not just ask a model to review a repository?
You can, and for a focused question that may be enough. fractureai is built for investigations that continue beyond a single conversation: it keeps jobs, experiments, costs, transcripts and evidence together. The value is in running and reviewing the research, not simply generating a list of possible bugs.
Is fractureai an API wrapper or an agent harness?
fractureai uses model APIs and an agent execution loop. Around that it provides durable job management, isolated test environments, execution controls, searchable traces and retained evidence. Those are the capabilities to judge it on. A model response alone is not treated as a verified finding.
How is a hypothesis different from a finding?
A hypothesis is a question worth testing. An experiment may support it, rule it out or leave it unresolved. A candidate finding needs reproduction and review, with the relevant requests, responses, source references and artifacts attached. Even a reproduced result still needs a person to assess scope, impact and disclosure.
Does this replace a security researcher or a full audit?
No. Models can miss issues, misunderstand code and produce false positives. fractureai helps organize and carry out research; it does not guarantee complete coverage or a vulnerability-free result. Researchers remain responsible for authorization, interpreting evidence and deciding what to report.
What is different from other AI security tools?
The emphasis here is traceable, agent-driven research: explicit job controls, full tool transcripts and evidence that can be retrieved independently of a chat. Other tools may offer similar capabilities. Compare them on your own targets, including reproducibility, false positives, retained evidence and actual operating cost. We do not claim universal superiority.
What does the hunt preview demonstrate?
It illustrates a permission-boundary investigation: hypotheses emerge, tests rule some out, and a supported candidate is reproduced. Playback is condensed. The displayed token usage, cost and outcome describe this example; they are not a benchmark, a price quote or a promise of results.
Can I operate it through Codex or OpenCode?
Yes. The platform exposes APIs and MCP tools for job management, controls and evidence retrieval. An authorized agent can use the same records and operations as the dashboard. A submitted command and the worker’s acknowledgement remain distinct, so an agent can check whether an action actually happened.
How are costs and sensitive research data handled?
Hunts have explicit budgets and recorded usage so you can inspect spending. Token costs depend on the model and workload; a budget is not a guarantee of a finding. Research traces can contain sensitive code or output. Access is restricted, and operators must configure provider choices, permissions and retention for their deployment.
Can I sign up today?
Public accounts are not open yet. Where launch notifications are enabled, you can request an email and confirm it separately. This creates no research account.
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