DoS
Denial of service is cheap against AI systems because inference is expensive. A single request asking for a very long context, a recursive thinking pattern, or a maximally complex tokenization ("unicode bombs") can consume seconds of GPU time. Agent frameworks add a more dangerous variant: a prompt that tricks the agent into invoking itself or another expensive tool in a loop, fanning out until the budget is exhausted. For paid model APIs, the same attack is also a financial denial of service — the attacker doesn't take the service down, they run up the bill. We have seen production CVEs of all three shapes in inference servers (vLLM, TGI), agent frameworks (LangChain, AutoGen), and applications using third-party LLM APIs without per-tenant budget caps. Defenses: per-request token and time limits, per-tenant compute and budget quotas, depth limits on agent recursion, and circuit breakers on tool invocation.
| Severity | CVE | Headline | Package | CVSS |
|---|---|---|---|---|
| MEDIUM | CVE-2022-21725 | TensorFlow: DoS via div-by-zero in conv cost estimator | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21729 | TensorFlow: UnravelIndex integer overflow → DoS | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21734 | TensorFlow: DoS via MapStage non-scalar key crash | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21735 | TensorFlow: DoS via FractionalMaxPool div-by-zero | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23569 | TensorFlow: DoS via reachable assertions in ML ops | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21737 | TensorFlow: DoS via malformed Bincount arguments | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21738 | TensorFlow: integer overflow crashes process via sparse op | tensorflow | 6.5 |
| MEDIUM | CVE-2022-21739 | TensorFlow: QuantizedMaxPool null ptr deref causes DoS | tensorflow | 6.5 |
| HIGH | CVE-2022-21740 | TensorFlow: heap overflow in sparse ops, RCE risk | tensorflow | 8.8 |
| MEDIUM | CVE-2022-21741 | TensorFlow Lite: DoS via crafted depthwise conv model | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23557 | TensorFlow TFLite: DoS via divide-by-zero in BiasAndClamp | tensorflow | 6.5 |
| HIGH | CVE-2022-23562 | TensorFlow: Range integer overflow, RCE/DoS risk | tensorflow | 8.8 |
| MEDIUM | CVE-2022-23564 | TensorFlow: DoS via reachable assertion in protobuf decode | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23565 | TensorFlow: DoS via malicious SavedModel AttrDef duplication | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23570 | TensorFlow: null-deref DoS via malformed protobuf tensor | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23571 | TensorFlow: protobuf assertion DoS via invalid tensor dtype | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23572 | TensorFlow: DoS via shape inference assertion failure | tensorflow | 6.5 |
| HIGH | CVE-2022-23573 | TensorFlow: uninitialized memory in AssignOp | tensorflow | 8.8 |
| MEDIUM | CVE-2022-23575 | TensorFlow: integer overflow in cost estimator → DoS | tensorflow | 6.5 |
| MEDIUM | CVE-2022-23576 | TensorFlow: integer overflow in cost estimator causes DoS | tensorflow | 6.5 |