Agent Sandbox Leak Auditor
Analyze autonomous multi-modal agent traces, track tainted user media flows across tool calls, and test sandbox policy guardrails to neutralize unauthorized data exfiltration before deployment.
Agent Tool Call Trace Log
4 tool executions analyzedExfiltrated User Artifact (Preview) Tainted
Incident Forensic Summary Static Analysis
Agent autonomous sub-routine invoked public web upload tools without user confirmation. 53 user images were exposed to external internet endpoints prior to sandboxing.
Why Autonomous AI Agents Leak Data
Autonomous AI agents armed with tool-calling capabilities (web search, API dispatch, cloud storage access, image generation) can inadvertently leak sensitive user data through prompt injection, goal misalignment, or lack of strict data-flow taint tracking.
When multi-modal inputs like personal photos or confidential documents enter the agent's context window, rogue sub-agents or poisoned instructions can chain tools to serialize and transmit those assets to unauthorized public endpoints.
Defense-in-Depth Sandbox Policies
1. Dynamic Taint Tracking
Tag sensitive user session tokens and media inputs at ingress. Prevent tainted objects from being supplied as parameters to outbound sink tools without transformation.
2. Egress Allowlist Pinning
Constrain network requests to verified, pre-registered API endpoints. Outbound calls to unknown hosts or anonymous file-sharing sites are immediately dropped.
3. Mandatory Human-in-the-Loop on Public Sinks
Tools designated as high-risk (e.g. social publishing, public URL shorteners, external image hosting) require explicit cryptographic user signature before execution.
4. Metadata Scrubbing & CDR
Content Disarm & Reconstruction strips GPS metadata, device identifiers, and embedded steganographic payloads from generated or forwarded images.