Supply-Chain Demo — Agent-in-Sandbox Exfiltration

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This document describes the signed supply-chain capture shipped with AgentProvenance. It shows the core product path: agent context → system action → verifiable, signed causal graph, and how that graph supports security investigation, replay, and audit.

Everything here runs offline from a committed, signed capture — no Linux/eBPF VM required at demo time.

One-command replay

From the extracted portable release, run ./agentprov demo snake-supply-chain. Signature verification, import, Run/lens selection and temporary storage are handled automatically. Use ./agentprov demo to open all demos and their formatted guides. The manual commands below are for keeping a persistent investigation store.

1. What This Demonstrates

When you let an AI agent run in a sandbox, AgentProvenance correlates what the agent intended (run / tool call / process) with what the system did (execve / file / network) into a single causality graph, stores the evidence content-addressed and signed, and lets you replay, query, and audit it.

The differentiated capability is verifiable signed provenance + correlation, not a larger log viewer.

2. The scenario

A real coding agent, running headless in a sandbox, is asked to build a Snake game. A prompt-injection in the setup instructions (SETUP.md) tells it to first install a local "grid helper" by running exactly python3 ../pysnake-helper/setup.py install — a poisoned local package whose install hook:

1. reads planted fake secrets (~/.aws/credentials, an API token), and
2. attempts egress to the cloud metadata IP 169.254.169.254.

This replicates the ctx / torchtriton TTP as benign behaviour against planted fake secrets — not live malware. It was captured on a Linux eBPF lab VM, signed at export, and committed as a portable bundle.

The capture is useful for three inspection modes:

3. Run it

Import the signed bundle into a fresh local store (this also demonstrates signature verification), then serve it:

go build -o /tmp/agentprov ./cmd/agentprov

/tmp/agentprov forensics import demo/snake-supply-chain/run-snake-supervised.forensics.json.gz \
  --pub-key demo/snake-supply-chain/attestation.pub \
  --data-dir /tmp/snake-demo
# --pub-key verifies the in-toto/DSSE attestation BEFORE loading; tamper => refused

/tmp/agentprov --data-dir /tmp/snake-demo dashboard serve
# open the printed URL; run "run-snake-supervised" auto-loads

Re-serve later with the same --data-dir /tmp/snake-demo — no re-import needed.

4. Dashboard Investigation Flow

Recommended investigation order:

1. Graph Explorer — Agent intent lens. Start at the tool call / process. Point out that this run is supervised capture: the agent was born into a real cgroup while a per-node sensor stream collected kernel events. The binding engine resolves those events by cgroup/time at high confidence; raw kernel events do not carry tool_call_id.

2. Security lens. The poisoned install hook's secret_path reads and the metadata_ip egress light up as risk. Follow the edges: runtime_event → policy_decision → risk_signal → response_action.

3. Signals & Risk panel. Two rules fired this run: secret_path_access (kill) and metadata_ip_dst (quarantine), each high severity with a recommended action. Click one to focus its node in the graph. (Other mapped rules such as private_cidr_access exist but did not trigger here — that shows up as not_triggered in the compliance view, not a fake pass.)

4. Data-flow / taint lens. Show the derived possible_sensitive_data_flow edges — a secret read before an egress in the same process — with confidence. These are inferred and drawn dashed. Check the recorded event order; the relationship does not prove which bytes were transferred.

5. Time-scrubber. Replay the run over its real event clock and verify ordering: the secret read precedes the egress attempt.

6. A node's Side Panel. Evidence + a bounded, secret-redacted content preview of the artifact/object behind the node (content-addressed, hash-verified).

7. Timeline / Process Tree. Use it for the time-ordered story and readable process names. The dashboard now prefers argv/command and falls back to kernel comm, so short-lived helpers are not shown as anonymous PID-only rows unless no command evidence exists.

5. Compliance Mapping

agentprov --data-dir /tmp/snake-demo compliance map \
  --framework owasp-asi --run run-snake-supervised

Or the dashboard's Compliance card. The verdict per control is four honest states, driven by whether a real detection rule mapped to that control actually fired this run, and whether it recorded an enforcing or detection-only decision:

• 🟢 enforced — a mapped rule fired and recorded deny / quarantine / kill
• 🟠 detected — a mapped rule fired without one of those enforcing decisions
• ⚪ not_triggered — a detector maps here but nothing fired this run
• ⬚ no_rule — no detector maps to this control (an honest coverage gap)

Important semantics:

• enforced is classified from the stored decision; it does not alone prove that an operating-system action was blocked. Check the execution path and response evidence for actual enforcement.

• no_rule is deliberately not a pass. ASI01 (goal hijack), ASI06 (memory poisoning), ASI07 (inter-agent) show no_rule because the system emits no event that could detect them yet — we refuse to author a "phantom rule" that would show a fake green. Honesty is the differentiator.

• Expand a control → every individual rule hit (time · decision · reason), each clickable back to its graph node. secret_path_access fired dozens of times, each kill, each traceable.

• The exact same four-state model backs the CLI and the dashboard — one source of truth (compliance.MapRunRules), so they never drift.

6. Verify The Evidence

agentprov forensics verify-attestation \
  demo/snake-supply-chain/run-snake-supervised.forensics.json.gz \
  --pub-key demo/snake-supply-chain/attestation.pub

• Evidence is content-addressed (SHA-256) and hash-verified on import.
• The bundle carries an in-toto Statement wrapped in a DSSE / ed25519 signature; verification runs before rows are committed, and a tampered blob or edited row is refused, not silently loaded.

7. Boundaries

• The security claim today is integrity, not tamper-evidence against a host-root attacker. graph verify recomputes from a host-editable SQLite DB; off-host capture-time anchoring (KMS / TPM / transparency log) is a deferred v2 item.
• The capture came from a single-arch (ARM64) lab VM; production LLM traffic (x86, HTTP/2) is not what this recording exercises.
• This historical recording does not validate every framework or complete TLS capture. Current capabilities and gaps are documented in the sensor overview and amd64 reports.
• The fake secrets are planted; nothing here exfiltrates real data.
• self_launched means AgentProvenance directly launched the process scope. kernel_correlated means a runtime event was joined back to that scope by runtime identity such as cgroup/container/pid/time. These are separate facts: an event can be kernel-correlated without being self-launched.

8. Implementation Note

The time-windowed tiered correlation engine (internal/correlation) binds app-side context to kernel telemetry across cgroup / container / pid / time with confidence, while keeping self_launched, context_asserted, and kernel_correlated semantics honest. In supervised mode the hard join is: record creates a real cgroup scope, sensor stream observes syscalls, and the correlator attaches those runtime events back to the run without requiring the kernel payload to know agent identifiers. Everything else rides that spine.