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 |
|---|---|---|---|---|
| HIGH | CVE-2022-35971 | TensorFlow: DoS via invalid quantization tensor rank | tensorflow | 7.5 |
| HIGH | CVE-2022-35972 | TensorFlow: DoS via QuantizedBiasAdd rank validation | tensorflow | 7.5 |
| HIGH | CVE-2022-35973 | TensorFlow: DoS via QuantizedMatMul input validation | tensorflow | 7.5 |
| HIGH | CVE-2022-35974 | TensorFlow: DoS via nonscalar quantization op input | tensorflow | 7.5 |
| HIGH | CVE-2022-35979 | TensorFlow: DoS via nonscalar input in QuantizedRelu | tensorflow | 7.5 |
| HIGH | CVE-2022-35981 | TensorFlow: DoS via FractionalMaxPoolGrad assertion | tensorflow | 7.5 |
| HIGH | CVE-2022-35982 | TensorFlow: DoS via invalid SparseBincount input | tensorflow | 7.5 |
| HIGH | CVE-2022-35983 | TensorFlow: DoS via Save/SaveSlices dtype CHECK fail | tensorflow | 7.5 |
| HIGH | CVE-2022-35984 | TensorFlow: int64 type mismatch triggers remote DoS | tensorflow | 7.5 |
| HIGH | CVE-2022-35985 | TensorFlow: DoS via malformed LRNGrad tensor input | tensorflow | 7.5 |
| HIGH | CVE-2022-35986 | TensorFlow: RaggedBincount DoS crashes inference server | tensorflow | 7.5 |
| HIGH | CVE-2022-35987 | TensorFlow: DoS via DenseBincount shape mismatch | tensorflow | 7.5 |
| HIGH | CVE-2022-35988 | TensorFlow: GPU DoS via empty input to matrix_rank op | tensorflow | 7.5 |
| HIGH | CVE-2022-35989 | TensorFlow: MaxPool GPU kernel DoS via oversized ksize | tensorflow | 7.5 |
| HIGH | CVE-2022-35990 | TensorFlow: DoS via quantization gradient rank check | tensorflow | 7.5 |
| HIGH | CVE-2022-36018 | TensorFlow: RaggedTensor CHECK fail remote DoS | tensorflow | 7.5 |
| HIGH | CVE-2022-36019 | TensorFlow: DoS via FakeQuant tensor rank mismatch | tensorflow | 7.5 |
| HIGH | CVE-2022-36026 | TensorFlow: DoS via QuantizeAndDequantizeV3 CHECK fail | tensorflow | 7.5 |
| HIGH | CVE-2022-35991 | TensorFlow: DoS via TensorListScatter CHECK fail | tensorflow | 7.5 |
| HIGH | CVE-2022-35992 | TensorFlow: DoS via malformed TensorList element shape | tensorflow | 7.5 |