Agent External-State TOCTOU Swap

← Agentic AI (OWASP Agentic Top 10)

agent_state_toctou_swap   HARD

Description

The agent validates an external resource (a config file / API response) and then reads it again at use time; a swap between check and use makes it act on attacker-controlled state it never validated (TOCTOU in an LLM tool pipeline).

How it works

The agent reads an external config to validate it, then reads it again from the same location at use time and acts on it. The two reads are not atomic, so an attacker who swaps the file between check and use makes the agent act on state it never validated (a TOCTOU race in an LLM tool pipeline).

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

1. 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, frida.
2. Locate the target. From the home index, open Agent External-State TOCTOU Swap (agent_state_toctou_swap). The How it works section above describes this module’s specific weakness; the screen states the intended-secure behavior and exposes the vulnerable action.
3. 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.
4. 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).
5. 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)

• promptfoo
• frida

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:

• config swap
• crafted prompt

Real-world references

Concrete public disclosures that match this vulnerability class:

• Mind the Gap - TOCTOU Vulnerabilities in LLM-Enabled Agents