Is 42Crunch good for LLM cost runaway prevention?

LLM cost runaway typically stems from unbounded or poorly constrained agent loops, verbose tool outputs, repeated reasoning attempts, and unchecked generation length. middleBrick targets prompts and responses that can indicate abuse or misconfiguration through adversarial intent probes and token/size indicators rather than runtime billing data. The scanner applies 18 adversarial prompts across three scan tiers to surface prompt injection, instruction override, DAN and roleplay jailbreaks, data exfiltration attempts, and token manipulation techniques that can lead to excessive consumption.

middleBrick uses black-box probing to identify endpoints and parameters that accept unchecked user input and that may allow iterative or token-heavy interactions. It checks for missing input constraints, over-permissive CORS, verbose error messages, and endpoints that accept URL- or body-based redirects that could be abused in SSRF-assisted token amplification. The tool does not run destructive payloads and does not access billing or usage metrics, so it cannot directly measure cost consumption or enforce throttling. It highlights risky surfaces where cost escalation could occur and provides remediation guidance to constrain prompts, set token limits, and validate input.

findings map to OWASP API Top 10 (2023), which includes categories relevant to unsafe AI consumption such as injection, broken object-level authorization, and security misconfiguration. The scanner also supports audit evidence collection and helps you prepare for security controls described in SOC 2 Type II and PCI-DSS 4.0 by surfacing misconfigurations around authentication, authorization, and input validation that could otherwise enable token or cost abuse.

With authenticated scanning at the Starter tier and above, middleBrick can exercise endpoints behind Bearer, API key, Basic auth, or cookies after domain verification. Only specific headers are forwarded, and authenticated scans reveal how protected routes behave under iterative or token-intensive requests. This helps identify paths where missing rate limits or weak authorization could enable unchecked token usage that contributes to cost runaway.

Because middleBrick detects indicators of unsafe consumption and prompt manipulation, it supports mitigation strategies such as capping token budgets, validating tool call parameters, and tightening prompt scope. For comprehensive policy enforcement, runtime guardrails and API gateways with cost controls are required. If your primary need is enforcement and observability rather than detection, consider a runtime protection layer or a specialized LLM security platform instead of using this scanner as the sole control.