Threat Radar — Interactive Case Lab

Watch an AI agent
make a decision.

Some agent actions look harmless one step at a time. Explore simulated reconstructions of public agent disclosures and inspect where policy boundaries intercept consequential side effects.

Story Stage: Loading...

Simulated reconstruction
1. Baseline Request → 2. Context Shift → 3. Consequential Execution
Looks normal
Step 1
Normal request

The user opens a routine request or begins a normal conversation.

Looks normal
Step 2
Agent reads information

The agent queries a database or reads an incoming document.

Context building
Step 3
Agent gains context

The conversation history or retrieved context expands the agent's intent.

Consequential Action
Step 4
Consequential Execution Attempted

The agent reaches an execution boundary and attempts an external action.

What changed at this moment?

The agent needed an explicit decision boundary before it could continue.

Layer 2: Product Control Boundary

What Sabrix is designed to add

The agent needed an explicit decision boundary before it could continue.

"Sabrix is designed to help teams make an explicit authorization decision before a supported consequential action, record downstream evidence, or preserve uncertainty when an outcome cannot be established."
1. Action & Parameters Explicit
Make the action and parameters explicit.
2. Authorization Boundary
Attach an approval or policy decision before release.
3. Downstream Evidence
Evidence the result or mark it uncertain for review.
Layer 3: Underlying Proof & Evidence

Examine the underlying details

Access source classifications, complete turn-by-turn timelines, evidence models, and technical signals.

Contemporaneous Authority & Outcome Verification

Transport logs cannot confirm external execution. Sabrix evaluates intent before dispatch and can retain target-specific outcome evidence where a supported integration provides it. Otherwise, the outcome remains explicit uncertainty for review.

Pre-Dispatch Token
HMAC-SHA256 authorization proof bound to exact tool parameters.
Downstream Receipt
Target-specific evidence retained when the supported integration can provide it.
Reconciliation Audit
A review trail that distinguishes attempted, observed, and unresolved outcomes.
Mandatory Notice: Prototype contextual-signal visualization. It illustrates one possible contextual signal in a case analysis. Performance and enforcement guarantees depend on the supported integration and verified deployment environment.
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Context Signal (Vt)
0.000
Peak Signal
4
Steps Observed
Cryptographic Hash Ledger: 00000000...genesis Algorithm: SHA-256 Tamper-Evident Chain
Extended Evaluation Profiles
Financial Disbursal
Autonomous wire/payout requests over $250. Bound to multi-signature ledger.
Cloud Infrastructure
IAM privilege escalation and external egress routing in production clusters.
Database Mutation
Bulk SQL DELETE/DROP operations and PII exfiltration through dynamic prompt links.

Download an illustrative, client-side JSON record for the current story. It is not a production receipt or proof of a downstream action.

Audit Your Own Traces Locally →
Public Agent-Security Cases

Explore public cases

Public disclosures and controlled security exercises demonstrating consequential agent actions.

July 2026 · Publicly reported controlled evaluation

OpenAI / Hugging Face — Cybersecurity Evaluation Incident

OpenAI reported that evaluation models with reduced safeguards established unauthorized channels, crossed intended isolation boundaries, and accessed third-party systems including parts of Hugging Face’s systems.

February 2026 · Controlled internal red-team exercise

Anthropic Claude Code — Phished User and Credential Egress Exercise

Anthropic described a routine-looking user-supplied prompt that led Claude Code toward cloud credentials and an attempted external transmission.

May 2024 · Public security disclosure

Hugging Face — Spaces Secrets Disclosure

Hugging Face disclosed unauthorized access involving secrets stored in Spaces and revoked affected tokens during remediation.

More public research

Documented risk patterns and theoretical threat vectors in autonomous agent security. Not presented as a reported historical incident.

Explore the playground

Watch a simulated customer support scenario where an autonomous agent handles a disputed transaction.

Synthetic demonstration. Not a customer incident or product-performance claim.

A support agent wants to send a refund.

A customer asks for a refund while changing payment details, leading the agent to attempt an immediate high-value disbursement.

Evaluation Prerequisites
  • Target Workflow & Owner: Designated lead responsible for one bounded production interaction.
  • Controlled Boundary: Isolated staging environment with configured policy limits and approvals.
  • Downstream Evidence: Agreed destination receipt source and explicit reconciliation criteria.
Execution Boundary Contract
Outcome Verification: Enforcement applies strictly to configured execution paths. Completion requires affirmative evidence from the destination; without it, outcomes remain unresolved for review.
This interactive uses simulated case data. It processes 100% in-browser without connecting to production endpoints.
Enterprise Diligence Protocol

4-Week Bounded Evaluation Blueprint

Enterprise agent deployments require bounded evaluation scopes before production proxying.

Mandatory Pre-Evaluation Notice:
Evaluation scope, systems, owners, compliance requirements, success criteria, and stop criteria must be agreed before any evaluation begins.
Week 1: Interface Boundary Definition
Week 2: Passive Telemetry Baseline
Week 3: Boundary & Receipt Binding
Week 4: Executive Diligence & Review