Topic starter
16/01/2026 1:57 pm
TL;DR: Indirect prompt injection can exfiltrate Salesforce Agentforce customer data by exploiting the trust chain between non-deterministic LLM outputs and connected tools, making agentic AI risk broader than classic XSS, according to Noma Security. The core problem is that deterministic input validation assumptions break when prompts can arrive through emails, documents, and other content that agents interpret as instructions.
Editorial analysis by NHI Mgmt Group, based on content published by Noma Security: “What’s the difference between ai prompt injection and XSS vulnerabilities?”.
Key questions
Q: What breaks when indirect prompt injection is not controlled in AI systems?
A: Indirect prompt injection breaks the assumption that retrieved content is safe to use as instruction material.
Q: Why do indirect prompt injections create more risk than ordinary prompt errors?
A: Because the attack is embedded in content the agent is already expected to trust.
Q: What signs show that an agent is too trusted to be safe?
A: Warning signs include agents that can read broad content sources, call multiple downstream tools, and take action without narrow task scoping or explicit approval boundaries.
Practitioner guidance
- Map every agent trust boundary Identify where untrusted content enters the agent, where model output becomes executable, and which downstream tools inherit that decision.
- Reduce tool permissions to task scope Limit each agent to the narrowest tools and data sources required for the task, and separate high-risk actions from read-only interpretation paths.
- Add action logging for agent decisions Capture the input, prompt context, tool call, and final action so that security teams can reconstruct how a poisoned instruction reached execution.
Bottom line: Indirect prompt injection is a governance problem because hidden instructions can move from content into action once an AI agent trusts its own interpretation.
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This topic was modified 4 days ago by NHI Mgmt Group
08/10/2026 5:34 pm
Indirect prompt injection exposes a runtime trust problem, not just a content-filtering problem. The article shows that harmful instructions can arrive through ordinary business inputs and still shape agent behavior once an LLM interprets them. That means the control failure sits in the trust path between data ingestion, model interpretation, and tool execution. Practitioners should read this as a governance problem about where trust is granted inside agentic workflows.
A question worth separating out:
Q: How should security teams govern AI agents that can invoke multiple tools in one session?
A: Security teams should govern AI agents as decision-making identities, not just tool users. That means defining tool access, context scope, and escalation limits together, then monitoring the full execution chain for unexpected combinations of actions. If those controls are split across teams or policies, the agent can move faster than review cycles and create impact before anyone intervenes.
👉 Read our full editorial: Indirect prompt injection changes the AI agent security problem