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-2022-29212 | TensorFlow Lite: quantization assert crash (DoS) | tensorflow | 5.5 |
| MEDIUM | CVE-2022-29213 | TensorFlow: input validation DoS in FFT signal ops | tensorflow | 5.5 |
| HIGH | CVE-2022-35934 | TensorFlow: tf.reshape DoS via integer overflow | tensorflow | 7.5 |
| HIGH | CVE-2022-35935 | TensorFlow: DoS via SobolSample CHECK-failure | tensorflow | 7.5 |
| CRITICAL | CVE-2022-35937 | TensorFlow: GatherNd OOB read crashes inference servers | tensorflow | 9.1 |
| CRITICAL | CVE-2022-35938 | TensorFlow: OOB read in GatherNd causes crash/data leak | tensorflow | 9.1 |
| CRITICAL | CVE-2022-35939 | TensorFlow: ScatterNd OOB write enables RCE/crash | tensorflow | 9.8 |
| HIGH | CVE-2022-35940 | TensorFlow: integer overflow in RaggedRangeOp crashes service | tensorflow | 7.5 |
| HIGH | CVE-2022-35941 | TensorFlow: DoS via negative ksize in AvgPoolOp | tensorflow | 7.5 |
| HIGH | CVE-2022-35952 | TensorFlow: DoS via UnbatchGradOp assertion crash | tensorflow | 7.5 |
| HIGH | CVE-2022-35959 | TensorFlow: DoS via AvgPool3DGradOp input overflow | tensorflow | 7.5 |
| HIGH | CVE-2022-35960 | TensorFlow: DoS via malformed TensorListReserve input | tensorflow | 7.5 |
| HIGH | CVE-2022-35963 | TensorFlow: DoS via FractionalAvgPoolGrad overflow | tensorflow | 7.5 |
| HIGH | CVE-2022-35964 | TensorFlow: remote DoS via BlockLSTMGradV2 validation | tensorflow | 7.5 |
| HIGH | CVE-2022-35965 | TensorFlow: NULL deref DoS via empty tensor input | tensorflow | 7.5 |
| HIGH | CVE-2022-35966 | TensorFlow: DoS via QuantizedAvgPool input validation | tensorflow | 7.5 |
| HIGH | CVE-2022-35967 | TensorFlow: DoS via QuantizedAdd tensor rank flaw | tensorflow | 7.5 |
| HIGH | CVE-2022-35968 | TensorFlow: DoS via AvgPoolGrad shape validation failure | tensorflow | 7.5 |
| HIGH | CVE-2022-35969 | TensorFlow: DoS via malformed Conv2DBackpropInput | tensorflow | 7.5 |
| HIGH | CVE-2022-35970 | TensorFlow: DoS via malformed QuantizedInstanceNorm tensors | tensorflow | 7.5 |