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GHSA-j659-8xh6-5pq5
atomic-agents-stack_estimate_batch_cost (atomic_agents/agent.py) looks up the per-model output price with PRICING.get(model, {}), returning 0.0 for any model not in the hardcoded pricing table. _check_batch_reservation then early-returns when the reservation is <= 0, skipping the batch reservation entirely. That reservation is the only defense against the documented fan-out race where every parallel helper/delegate reads the identical pre-batch on-disk cost total and each passes its individual check even though the collective spend overruns the configured cap.
Impact: an operator running any model not in the pricing table (self-hosted/Ollama/vLLM, a new provider SKU) with cost_guardrails + daily_cap_usd set believes the cap protects them, but a single parallel batch can blow past the cap. The parallel-helper model argument can also be steered to an unknown id. The sibling dream._estimate_dream_cost does this correctly (PRICING.get(model, _fallback_pricing())), which makes this a clear defect.
Affected: agent.py (_estimate_batch_cost / _check_batch_reservation), all versions through 1.0.0.
Fix: use PRICING.get(model, _costs._fallback_pricing())['output'] (mirror dream/calc_cost). Add a conformance test asserting an unknown-model batch reserves > 0 and that an over-cap unknown-model batch raises CostGuardrailBlocked.
The vulnerability can be exploited over the network without needing physical access. It is easy for an attacker to exploit this vulnerability. An attacker does not need any special privileges or access rights. No user interaction is needed for the attacker to exploit this vulnerability.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
Probability that this vulnerability will be exploited in the wild within the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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