AI Resource Exhaustion Attacks

Resource exhaustion attacks target the infrastructure supporting AI models by overwhelming system resources such as memory, processing power, or API usage quotas. These attacks, a form of denial-of-service (DoS), can degrade performance, interrupt service availability, or expose the system to secondary vulnerabilities.

In the context of AI, these attacks often exploit:

• Large payloads: Supplying extremely long prompts or datasets that push memory or token limits.
• Recursive tasks: Designing prompts that trigger computationally expensive operations (e.g., repeated summarization, code generation, or chain-of-thought reasoning).
• Rapid queries: Spamming APIs or inference endpoints with high-frequency requests to drain usage quotas or block legitimate users.
• GPU saturation: Overloading model-serving infrastructure through parallel jobs, causing latency spikes or model timeouts.

These tactics are particularly relevant in environments where large language models, generative AI, or real-time inference services are exposed via APIs. Even well-intentioned users can unknowingly trigger resource exhaustion through prompt misuse or poorly scoped tasks.

Unlike traditional DoS attacks that target network bandwidth, resource exhaustion leverages the computational demands of AI models themselves. This makes detection and mitigation more complex, especially in multi-tenant systems or when usage patterns vary widely.

To defend against these attacks, organizations must implement usage limits, behavioral monitoring, rate controls, and fallback mechanisms. More advanced defenses can profile input cost, detect patterns of abusive usage, and dynamically allocate resources based on priority or risk.

How PointGuard AI Addresses This:
PointGuard AI monitors model-serving environments for anomalies that indicate resource exhaustion and can alert teams when capacity thresholds are at risk. With PointGuard, organizations can protect availability while ensuring critical AI services remain stable and secure.

Resources:

CWE-400: Uncontrolled Resource Consumption

OWASP Top 10 LLM Risks