Unbounded Consumption Auditor

@guardbee/mcp-unbounded-consumption-auditor

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An MCP (Model Context Protocol) server that scans LLM/agent application code for Unbounded Consumption — OWASP LLM Top 10 2026's #6 category, also known as "denial of wallet": nothing stops the application from making unlimited billable calls.

Telemetry is off by default. With GUARDBEE_TELEMETRY=1 it sends the server, tool, success, duration and short parameter values such as a file path; longer strings and fields like content, token or password are replaced with a placeholder. See @guardbee/mcp-telemetry.

Claude ──► unbounded-consumption-auditor ──► your LLM/agent source

│ ├─ missing-token-limit (no cap on how much the model can generate) ├─ missing-timeout (a hand-rolled call to an LLM endpoint that can hang forever) ├─ unbounded-loop (an agent/retry loop with no iteration cap) ├─ disabled-safety-limit (a framework's own loop cap explicitly turned off) └─ missing-rate-limit (an MCP tool making billable calls with no throttling)

OWASP's own write-up on this category gives a concrete cost example worth keeping in mind while reading findings: *"A research assistant agent without any recursion limit or call budget ended up making 500 tool calls with a simulated cost of $10... An agent with a call budget, a recursion depth limit and an agentic circuit breaker... completed one tool call, spending just $0.02."*

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Why these five checks specifically

Every pattern here was checked against real SDK source or real code before being written — not guessed from training-data assumption.

• Missing token limit targets .chat.completions.create(...) calls (OpenAI Python/JS) with neither max_tokens nor max_completion_tokens set. It's deliberately scoped to OpenAI-shaped calls only: Anthropic's own SDK makes max_tokens a required constructor argument — it raises before the request is even sent — so a matching check there would be 100% false positives. Reasoning models (o1+) silently ignore max_tokens and need max_completion_tokens instead, so the check only flags a call missing *both*.
• Missing timeout targets hand-rolled requests/axios/fetch calls whose own argument span mentions a known LLM endpoint (api.openai.com, api.anthropic.com, /chat/completions, /v1/messages). The official OpenAI/Anthropic SDKs already ship a bounded client-level default (openai-node: 600s) — flagging those would just be noise. requests.post()'s own Quickstart warns that an unset timeout can leave a program waiting indefinitely; axios and bare fetch have the same no-default-timeout shape.
• Unbounded loop covers two real shapes, both real-code-verified: a hand-rolled while True:/while(true) agent loop that dispatches tool_calls with nothing nearby suggesting an iteration cap (the exact shape quoted from a public 70-line-agent walkthrough), and a retry loop that catches an exception, calls straight back into an LLM, and continues with no attempt counter (OWASP's own write-up calls this out by name: *"a failed step might be retried repeatedly"*). The retry-loop check is deliberately scoped to loops that actually call an LLM — an early version without that scoping flagged ordinary polling/retry code in a real open-source codebase during testing (see Development below).
• Disabled safety limit flags max_iterations=None (LangChain's AgentExecutor) and max_turns=None (openai-agents-python's Runner.run/run_sync) — both verified against real source before shipping. LangChain's AgentExecutor defaults max_iterations to 15, and its own _should_continue() only enforces the cap if self.max_iterations is not None — so None genuinely disables it. openai-agents-python's own issue tracker states plainly that max_turns=None disables the turn limit. LangGraph's `recursion_limit` has no equivalent — it requires a concrete number — so no pattern is shipped for it, rather than guessing at an unverified "disable" mechanism.
• Missing rate limit is the one heuristic pattern here, shipped at medium (not high/critical) severity on purpose: no MCP SDK (TypeScript or Python) provides a built-in per-caller throttling hook on tool registration, unlike the bounded defaults LangChain/LangGraph ship for iteration caps. Multiple independent 2026 write-ups describe the fix as a hand-rolled wrapper around server.tool()/@mcp.tool(), implying there's no framework default to fall back on. This check can't see a rate limiter enforced elsewhere (an API gateway, middleware in another file) — see Limitations.

Relationship to `ai-code-scanner`: that package already has a coarser unbounded_agent_loop pattern (any while(true)/for(;;) within 200 characters of a .chat.completions.create/.messages.create call, under its excessive-agency category) — it doesn't check whether the loop actually dispatches tool calls, and doesn't check for a nearby iteration-cap hint, so it would also flag a loop that's already properly bounded. This package's unbounded_tool_calling_loop is narrower and cost-specific: it requires an actual tool_calls/toolCalls reference (the real agentic-dispatch shape) and explicitly skips loops with a visible cap hint nearby.

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Features

• 7 patterns across 5 categories, each grounded in real SDK source, a real code walkthrough, or OWASP's own 2026 write-up — cited inline in the pattern comments
• 25 tests (positive + negative for every pattern, including the exact quoted real-world loop snippet the unbounded-loop checks are built from)
• SARIF 2.1.0 output — CI/CD integration (GitHub Code Scanning, etc.)
• End-to-end verified against a planted-vulnerability fixture (all 7 patterns caught, all 3 "safe counterpart" negatives correctly skipped) and against a real 3,414-file open-source codebase (IBM/mcp-context-forge) plus this monorepo's own 495 files — zero false positives in production code. Two real false positives *were* found and fixed during that verification, before shipping: an early, wider loop-body window let an unrelated except/continue elsewhere in the same file get mistaken for part of a nearby loop (fixed by requiring an LLM-call hint in the window and shrinking it), and a disabled max_iterations=None a few lines below a loop was briefly mistaken for a legitimate nearby iteration cap (fixed by stripping disabled-cap assignments before testing for a real one).

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Quick Start

Claude Desktop / MCP Client

{

"mcpServers": { "guardbee-unbounded-consumption-auditor": { "command": "npx", "args": ["-y", "@guardbee/mcp-unbounded-consumption-auditor"] } } }

CLI (CI/CD)

npx @guardbee/mcp-unbounded-consumption-auditor scan . --fail-on=high --format=sarif > results.sarif

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MCP Tools

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Detected Patterns

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Configuration (guardbee.yml)

unbounded-consumption-auditor:

fail-on: high # any | critical | high | medium | low | none max-files: 5000 exclude:

• ".test.ts"

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Limitations (by design)

• Bounded-window heuristics, not real control-flow analysis. The loop checks look at a fixed character window after while True:/while(true), not the actual loop body — a loop with an unusually long body, or one whose cap check lives outside that window, can be missed. Testing against real code found the reverse failure mode too (an unrelated except/continue further down getting mistaken for part of a nearby loop) and tightened the window accordingly; it's still a heuristic, not a parser.
• The rate-limit check can't see enforcement elsewhere. A tool handler with no rate-limiter in its own body might still be protected by an API gateway, middleware in another file, or a wrapper this scanner doesn't trace into — this check flags what's visible in the handler itself, nothing more.
• No cross-function default detection. If a project sets an output limit or timeout once, centrally, and every call site relies on that default implicitly (rather than passing it per-call), the per-call checks here will still flag each call site individually.
• LangGraph's `recursion_limit` isn't checked. Unlike LangChain and openai-agents-python, LangGraph's recursion limit requires a concrete number — there's no verified "set it to disable" mechanism, so no pattern is shipped for it rather than guessing at one.

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Development

npm run build

npm test # 25 unit tests

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License

MIT — GuardBee

Feedback

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