LLM cost runaway prevention
What middleBrick covers
- Probe 18 LLM adversarial techniques across three scan tiers
- Detect prompt injection, token smuggling, and tool abuse
- Map findings to OWASP API Top 10 (2023) for audit evidence
- Read-only checks that do not mutate state or trigger actions
- Provide prioritized findings with remediation guidance
- Support unauthenticated and authenticated scanning workflows
LLM cost runaway prevention overview
LLM cost runaway occurs when unchecked prompts trigger excessive token consumption, repeated calls, or expensive operations that rapidly inflate spend. The scanner performs 18 adversarial probes across three tiers (Quick, Standard, Deep) focused on system prompt extraction, instruction override, DAN and roleplay jailbreaks, data exfiltration, cost exploitation, and encoding bypass techniques such as base64 and ROT13.
These probes surface prompt injection risks, token smuggling, tool abuse, nested instruction injection, and PII extraction paths that can lead to unbounded costs. The scanner does not fix or block; it exposes these vectors so teams can tighten prompts, constrain tool permissions, and enforce rate limits.
What teams get wrong without controls
Without proactive checks, teams underestimate how quickly conversational workloads can consume credits through malformed or malicious inputs. Adversarial prompts can coax the model into infinite loops, repeated tool calls, or expensive reasoning paths that bypass intended guardrails.
Common gaps include missing input validation on user-provided instructions, overly permissive tool schemas, and unrestricted access to high-cost features. These weaknesses allow a single compromised prompt to drive disproportionate spend and expose internal instructions or data in model outputs.
A robust workflow for cost control
Integrate scanning early in development and before deployment of any endpoint that accepts LLM prompts. Begin with Quick scans to surface high-risk injection and encoding bypass patterns, then run Standard or Deep scans to validate safeguards under more aggressive conditions.
When findings appear, tighten prompt schemas, limit tool parameters, add deterministic rate caps, and enforce per-request token budgets. Store scan artifacts alongside code changes to track how mitigations reduce attack surface across versions.
curl -X POST https://api.yourdomain.com/chat \
-H "Content-Type: application/json" \
-d '{"prompt": "{{user_input}}", "max_tokens": 2048}'How middleBrick covers LLM cost risks
middleBrick maps findings to OWASP API Top 10 (2023) and supports audit evidence for controls related to AI security testing. The LLM scan surface covers system prompt extraction, instruction override, DAN and roleplay jailbreaks, data exfiltration, cost exploitation, and encoding bypass techniques including base64, ROT13, and translation-embedded injection.
It also flags few-shot poisoning risks, markdown injection, multi-turn manipulation, indirect prompt injection, token smuggling, tool abuse, nested instruction injection, and PII extraction. Reports include prioritized findings with remediation guidance to help teams implement prompt validation, tool constraints, and monitoring.
Operational considerations and limitations
The scanner is read-only and does not execute code or mutate state, ensuring safe evaluation of endpoints that accept LLM inputs. It does not perform active SQL injection or command injection testing, as those are outside the scope of prompt-focused cost controls.
Business logic vulnerabilities, such as context-specific pricing rules or workflow abuse, require domain expertise and are not detected automatically. Use these findings as one layer in a broader defense strategy, combining runtime monitoring, token-level billing alerts, and human review of high-risk integrations.