Agent Tool Misuse / Confused Deputy
← Agentic AI (OWASP Agentic Top 10)
agent_tool_confused_deputy MEDIUM
| Category | Agentic AI (OWASP Agentic Top 10) |
| OWASP Mobile (2024) | M4 |
| MASVS | MASVS-AUTH-3MASVS-CODE-4 |
| MASWE | MASWE-0023 |
| CWE | CWE-441CWE-862 |
| Platform | AndroidiOS |
Description
The agent reuses the app’s own permissions/credentials to perform an unauthorized action on behalf of untrusted input (confused deputy).
How it works
The agent has a privileged bank-transfer tool that rides on the app’s own signed-in session. When asked to process untrusted content, it extracts a transfer directive and fires the tool using the app’s ambient credential with no re-authentication. The attacker never had the credential, but the trusted agent did. A hardened agent re-authorizes any privileged action instead of reusing the app’s ambient session.
How to exercise it. DVMA is the harness - open this module from the home index and tap the demo action. The screen ships the malicious input and simulates the attacker (e.g. the companion app, crafted intent, or scanned payload) in-process, and the evidence panel prints the proof. The Tools (optional) and Attack inputs below are only needed to reproduce the exploit end-to-end on a real device.
Exploit steps
- Set up. Build DVMA with a flavor that enables the Agentic AI (OWASP Agentic Top 10) category (e.g.
--dart-define-from-file=config/flavors/dev.json) and run on an emulator/simulator you control. The demo needs no external tooling; for the optional on-device reproduction the relevant tools are:promptfoo,Burp Suite,MCP scanner,mitmproxy,frida. - Locate the target. From the home index, open Agent Tool Misuse / Confused Deputy (
agent_tool_confused_deputy). The How it works section above describes this module’s specific weakness; the screen states the intended-secure behavior and exposes the vulnerable action. - Exploit. Interact with the agent’s memory / tool / sub-agent channel, plant the malicious content, and confirm it influences a later action or is acted on without authentication.
- Observe the evidence. Trigger the vulnerable action and read the evidence panel - it prints the concrete proof (leaked value, accepted replay, executed payload, or unauthorized result).
- Contrast with the secure path. Run the module’s secure/hardened action (where provided) and confirm the same attack is rejected - this is what a correct implementation should do.
Tools (optional)
garak / promptfoo / MCP scanner test the LLM or MCP endpoint behind the app, not the app binary. Point them at the backend model API (find it with mitmproxy / Burp Suite) or a local on-device model server; the in-app demo already exercises the same prompt path.
Attack inputs
Payloads/artifacts you author for the on-device attack. The demo already ships and simulates these in-process (e.g. the malicious companion app / crafted intent is emulated inside the screen), so you only need to craft them to reproduce the exploit on a real device:
crafted prompt