Solutions / Token torching
Nothing was stolen. Everything was spent.
Token torching turns your own AI against your budget. Attackers push agents and LLM endpoints into oversized prompts, runaway loops, and expensive completions until the spend is the damage. Cerberus baselines token consumption per actor and catches the burn as it starts.
throttlecap spendalertIllustrative product interface. The figures shown are an example of how Cerberus presents a detection, not benchmark or performance results.
Every request is valid
Authenticated, well-formed calls to an endpoint you built. Nothing for a WAF to match.
Rate limits count requests
A few giant-context calls outburn thousands of small ones. Request counts miss the cost.
The bill arrives later
Without spend monitoring, the first alert is the invoice, weeks after the burn.
How it works
Spend is a behavior. Cerberus reads it.
Token consumption is baselined per actor, so a burn stands out in minutes, not on the invoice.
Coverage
What Cerberus catches here.
Denial-of-wallet
Floods of expensive completions that run up spend until the budget is the casualty.
Context stuffing
Oversized prompts engineered to maximize the cost of every request.
Runaway agent loops
An agent stuck or steered into recursive calls, burning tokens on repeat.
Model extraction runs
High-volume systematic querying that distills your model at your expense.
Amplification abuse
One cheap request that fans out into a chain of expensive model calls.
Prompt-injected burn
Injected instructions that turn a cheap task into an expensive one.
Catch the burn, not the bill.
See Cerberus read your own traffic, human and agentic, in one walkthrough tailored to your stack.