Evidence for your next AI security decision.
Understand what an attacker can do, which defences hold up and what they cost your workload. ProofSeam brings adversarial testing and traceable evidence to the decisions that matter: what to release, what to fix and what to trust.
Start with a scoped, specialist-led assessment.
Your AI works. What could make it fail?
An assistant can complete its task while exposing private data or taking an action it should not. ProofSeam helps you investigate the trust boundaries around the model, its data and its tools.
Can an input change the rules?
Examine how documents, prompts and tool responses influence agent behaviour.
Can a provider infer private data?
Investigate what a participant can learn from the representations or gradients it receives.
Does the defence preserve value?
Compare reduced exposure with the effect on task quality and compute requirements.
From a security question to evidence you can use.
A shared assessment process connects application behaviour, model privacy and defence trade-offs. Each engagement selects the tests supported by your architecture and available access.
Find where trust breaks
Test how malicious inputs, retrieved content and tool responses can influence an AI workflow. Investigate prompt injection, tool misuse, memory and approval handling in supported scenarios.
Application & agent security
Look beneath the model’s answers
Explore whether intermediate representations, training gradients or accumulated observations expose private information. Select privacy probes that match the model access available.
Model & data privacy
Compare protection with performance
Measure a defence against its baseline, alongside task quality and compute cost. See whether a mitigation reduces exposure while preserving the usefulness of the system.
Defence evaluation
Make findings reproducible
Retain configurations, run metadata, measurements and artifact references so engineers can retrace the finding. Evidence hashes support change detection against independently retained digests.
Traceable evidence
See what the assessment covers
Record the system version, attacker access, test budget and outcome for each scenario. Distinguish a demonstrated failure from a simulation, an unsupported test or a question still open.
Explicit coverage
Give the next decision an owner
Bring technical findings into a decision brief with remediation priorities, responsible owners and retest criteria. Keep the evidence engineers need alongside the consequences leaders need to understand.
Specialist interpretation
Go deeper into each attack and evaluation capability.
Read what each method tests, the access it requires and how to interpret its results. Browse reconstruction, agent security, model privacy, side channels and supporting evaluation tools.
Scope. Compare. Decide.
Start with one decision and follow the evidence through to remediation and retesting. Setloop’s specialists work with your engineering and security teams throughout the assessment.
- 01
Scope the decision
Choose one release, supplier or architecture question. Agree the system boundary, authorised access and test effects.
- 02
Set the baseline
Pin the configuration, evaluation data, test budgets and acceptance criteria before measuring outcomes.
- 03
Run and compare
Exercise the agreed scenarios against the baseline and mitigation. Measure attack outcomes, task quality and compute cost together.
- 04
Remediate and retest
Reproduce material findings, assign an owner and retest changes against the original criteria with independent data or runs.
- 05
Make the call
Review the evidence with your team: remediate, restrict deployment, gather more evidence or accept a documented residual risk.
A decision brief. An evidence trail. A path forward.
The assessment package gives leaders a clear account of exposure and gives engineers the detail needed to reproduce findings and evaluate changes.
Deliverables, data handling and retest scope are agreed before the assessment begins.
- Executive decision brief
- Material findings, business consequences, recommended actions and unresolved questions.
- Technical findings
- Affected configuration, attacker prerequisites, observed outcome and reproduction steps.
- Defence comparison
- Baseline and mitigation results alongside task quality, resource use and test budgets.
- Evidence package
- Run records, artifact references and verification information for technical review.
- Coverage & retest record
- Tests completed, failures and exclusions, with remediation owners and agreed retest criteria.
The right evidence for the people who must act.
Before you choose an architecture
Compare privacy defences and deployment options against the quality and compute constraints of your workload.
Before you release an agent
Investigate how untrusted content and tool access affect a named workflow, then reproduce and retest material failures.
Before you approve the risk
Review what was tested, what happened and what remains unresolved, with clear consequences and accountable owners.
Before you rely on a claim
Assess a supplier’s security or privacy assumptions against agreed access, evidence requirements and operating conditions.
Begin with one workflow. Build a repeatable practice.
ProofSeam is a Setloop product in development, built on the LLM-Attacker research engine. The initial engagement is a specialist-led assessment, with integration support and scope agreed for your environment.
A focused assessment
Bring a named AI workflow and a decision you need to make. We scope the access, applicable scenarios, baseline, mitigation comparison and evidence package with your team.
Timing and cost follow the integration work and agreed compute budget.
Retesting as your system changes
Use the first assessment to identify what should be tested again after a model, prompt, tool or infrastructure change. Agree follow-up work once the integration and baseline are repeatable.
Recurring validation is scoped with Setloop; a managed hosted platform remains in development.
Technical depth, with the boundaries made clear.
The LLM-Attacker engine provides attack modules, capture adapters, experiment runners and reporting tools. Evidence spans synthetic scenarios, bounded model experiments and selected model-driven harnesses. Each assessment states what was actually exercised.
The white paper explains the product direction, technical foundation and proposed assessment model.
Before we start.
Start with a specialist-led assessment scoped with Setloop. Bring one AI workflow, a release or procurement decision, and the access you can provide. We agree the supported tests, environment, deliverables and budget before work begins. ProofSeam’s managed product layer is in development; there is no self-service hosted platform to sign up for today.
Bring the system. Bring the question. Let’s build the evidence.
Tell us what you are releasing, buying or changing, and which security or privacy assumption you need to test.