Topic starter
02/08/2026 12:13 pm
TL;DR: A critical flaw in vLLM, CVE-2025-62164, lets attackers trigger remote code execution or a crash by sending a malicious API request, showing how unsafe deserialization in AI serving infrastructure can bypass standard SAST and WAF controls, according to Salt. The real control gap is not model safety but runtime trust in API payloads.
Editorial analysis by NHI Mgmt Group, based on content published by Salt: “Critical vLLM Flaw Exposes the Soft Underbelly of AI Infrastructure”.
Key questions
Q: What breaks when an AI serving API trusts serialized input too much?
A: The service can be forced to interpret hostile data as executable structure, which opens the door to memory corruption, denial of service, or remote code execution.
Q: Why do AI API payload flaws create more risk than prompt injection alone?
A: Prompt injection targets model behaviour, but payload flaws attack the infrastructure that parses, stores, and executes requests.
Q: How do security teams know if runtime controls are actually working for AI workloads?
A: They should be able to reconstruct a complete sequence from prompt ingestion to process activity to identity use to data access.
Practitioner guidance
- Harden deserialization paths in AI APIs Review every endpoint that accepts embeddings, tensors, or serialized objects and remove any parser behaviour that can reconstruct unsafe structures.
- Add AI-specific runtime validation Use request inspection that understands the expected shape of AI traffic so a malformed Base64 blob or object wrapper is rejected before it reaches the model server.
- Reduce inference server blast radius Place serving hosts in tightly segmented networks and limit their east-west and outbound permissions so one exploited endpoint cannot reach databases or internal services.
Bottom line: The core failure is unsafe trust in deserialized AI payloads, not a model-safety problem.
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02/08/2026 1:16 pm
API infrastructure, not model behaviour, is the first control boundary in AI security. The vLLM flaw shows that a serving stack can fail even when the underlying model is not the problem. That shifts governance from model output risk to request parsing, payload trust, and runtime isolation. Practitioners should treat the inference API as the security perimeter.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should teams do if an inference server can reach internal databases and APIs?
A: Treat that access as part of the attack surface, not an engineering convenience. Restrict outbound and east-west permissions, separate serving workloads from sensitive systems, and review the server’s workload identity so a single exploit does not become an environment-wide foothold.
👉 Read our full editorial: vLLM RCE shows AI security failures start in API infrastructure