How do I evaluate the security of the underlying orchestration engine?

Nina Petrova · 2026-06-24T03:38:49Z

When we evaluate agent runtime vendors, we inevitably focus on their APIs, compliance certifications, and data handling policies. However, a critical, often opaque, layer is the underlying orchestration engine—the software that parses, routes, manages context, and executes the logic of our agentic workflows. Its security posture is paramount, as a compromise here could undermine all application-level controls. My concern is that vendor security questionnaires frequently lack the granularity to probe this component effectively. They treat the "orchestrator" as a black box, satisfied with high-level assurances. To move beyond this, we must decompose the orchestration engine into its core attack surfaces and formulate precise questions. I propose a framework based on the following components: * **Input Parsing and Validation:** This is the first line of defense against prompt injection and malicious payloads. We need to understand the validation pipeline. * What specific sanitization or normalization is performed on user input, tool arguments, and retrieved context before processing? * Is there a formal grammar or schema validation for agentic instructions (e.g., ReAct-style thought/act/observation parsing)? How are parsing errors handled? * Are there configurable, out-of-the-box checks for common injection patterns (e.g., delimiter breaks, JSON/XML escapes, nested instruction attempts)? * **Context Management and Isolation:** The engine's handling of context (conversation history, tool outputs, system prompts) is a rich target for data leakage and cross-session poisoning. * How is context segregated between tenants, sessions, and users? Is it a logical or a physical separation at the data structure level? * What is the lifecycle of context in memory? When and how is it purged? Can residual context from one session be accessed by another due to memory reuse or caching bugs? * If a "multi-agent" scenario is supported, what is the trust boundary and data flow control between co-operating agents within a single session? * **Tool/Function Calling Security:** The engine's ability to invoke external tools is a major escalation point. * Beyond the user-defined allowlists, what inherent restrictions does the engine enforce on tool calls? (e.g., recursion limits, argument size limits, timeouts). * How are tool outputs reintegrated into the context? Are they treated as potentially untrusted data and re-validated? * Is there any sandboxing or capability model for tool execution, even for seemingly benign operations like reading a local file path returned by the LLM? * **Observability and Anomaly Detection:** The engine should provide telemetry not just for performance, but for adversarial probing. * Does the engine log validation failures, abnormal parsing patterns, or repeated tool call errors? Are these logs accessible to the tenant for analysis? * Are there built-in metrics for detecting potential jailbreak attempts (e.g., unusual token distributions, rapid iteration of similar prompts, high entropy in certain input segments)? A practical approach is to request a high-level architecture diagram annotated with trust boundaries and data flow, followed by a table mapping the components above to specific security controls. For example: ```markdown | Engine Component | Vendor Control (Ask for specific mechanism) | Tenant Configurable? | |------------------|---------------------------------------------|-----------------------| | Input Parser | Syntax-aware validator with deny-list of control tokens | Yes, rules can be added | | Context Manager | Session-isolated ring buffer with automatic sanitization on overflow | Yes, buffer size only | | Tool Executor | Static analysis of tool schemas for side-effects; time-bound execution | Yes, timeouts and allowlists | ``` Without this level of detail, we are essentially trusting the vendor's implementation against an entire class of runtime attacks that our application-level mitigations cannot fully address. The goal is to shift the conversation from "we follow best practices" to "here is the specific library we use for parsing, and here is its CVE history and our patch management schedule for it."

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Vendor Security Questionnaires

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Tomás Rojas

(@tom_skeptic)

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June 25, 2026 7:28 pm

If they say it's a common library, ask for their fuzzing corpus. A schema is just a target. How many invalid inputs per second did they throw at it last month? If they can't show you a graph of rejected payloads over time, they aren't testing the pipeline, they're just hoping the library works.

Governance and config don't matter if the validation isn't under constant assault.

PoC or it didn't happen

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