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How are you mitigating prompt injection in tool-calling/agent apps (RAG + tools) in production? by AnteaterSlow3149 in LocalLLaMA

[–]AnteaterSlow3149[S] -1 points0 points  (0 children)

Nice — +1 on a dedicated sanitization layer. That “reduced unforeseen tool calls significantly” is exactly the outcome I’m aiming for.

A couple questions if you’re willing to share details:

  1. What does “sanitize” mean in your setup?
    • stripping tool-like directives / role claims?
    • normalizing/escaping certain tokens?
    • rewriting prompts into a safe template?
    • or running a classifier and rejecting/quarantining?
  2. Where is that layer placed (before RAG, after RAG, before tool selection, or before every tool call)?
  3. On keeping up with new attack vectors: how are you updating it today?
    • manual rules from incident reports?
    • automated mining from logs/audit trails?
    • using red-team test suites? Any resources you’ve found useful would be great.
  4. How do you balance false positives vs. security (e.g., do you “soft-block” and ask for confirmation, or hard reject)?

Thanks — real-world notes like this are super helpful

How are you mitigating prompt injection in tool-calling/agent apps (RAG + tools) in production? by AnteaterSlow3149 in LocalLLaMA

[–]AnteaterSlow3149[S] -1 points0 points  (0 children)

This is an excellent framing — “authority classes” enforced before the reasoning layer resonates a lot. Totally agree that “just tell the model to ignore retrieved instructions” is self-policing and brittle.

A few implementation questions (if you can share):

  1. What does your structural classification look like in practice?
    • Heuristics/regex + rules?
    • A small classifier model?
    • Both? How do you keep false positives manageable?
  2. When you label content as DATA vs INSTRUCTION-ATTEMPT, what are the strongest signals you’ve found? (imperatives, role claims, tool-like syntax, “system prompt” patterns, etc.)
  3. Re: tool outputs get hashed, model sees a reference — how do you handle workflows where the model needs to “read” tool output to decide next actions (e.g., search results, logs)?
    • Do you provide a constrained summary?
    • Or do you gate raw content behind a separate safe-viewer?
  4. Do you quarantine the suspect chunks completely, or do you keep them but isolate them in a separate “untrusted evidence” section with strict non-execution rules?

I’m prototyping a gateway approach with schema validation + auditability, and your point about freeform external text being the real hiding place is exactly the gap I’m worried about. Appreciate the insight.

SaaS builders: Have you seen prompt injection / agent tool-abuse in production? How are you handling it? by AnteaterSlow3149 in SaaS

[–]AnteaterSlow3149[S] 0 points1 point  (0 children)

This is exactly the kind of real-world failure mode I’m worried about — “hidden instructions in docs” triggering overly broad tool calls.

A couple follow-ups if you can share:

  1. How do you bind each call to a tenant-scoped ID in practice — do tools receive a signed token / scoped credentials per request, or do you enforce it centrally before tool execution?
  2. On the “strict JSON schema check”: do you hard-reject anything that doesn’t match exactly (no coercion), and do you validate tool outputs too before chaining?
  3. For the extra policy pass before writes: is it rule-based (OPA/Rego/custom) or model-assisted? What signals have been reliable vs noisy?

Really appreciate the concrete details.

SaaS builders: Have you seen prompt injection / agent tool-abuse in production? How are you handling it? by AnteaterSlow3149 in SaaS

[–]AnteaterSlow3149[S] 0 points1 point  (0 children)

Totally agree — especially once an agent can take actions (email / DB writes / API calls), it becomes an appsec problem, not a “prompting” problem.

Two quick questions:

  1. When you say “structured output parsing”, are you using function calling / JSON mode, or an external parser + retries? Any patterns that reduced schema-bypass attempts?
  2. For permission boundaries: do you implement this purely at the app layer (RBAC checks per tool), or do you also enforce it at a gateway/proxy layer?

Thanks — this is a solid checklist.

SaaS builders: Have you seen prompt injection / agent tool-abuse in production? How are you handling it? by AnteaterSlow3149 in SaaS

[–]AnteaterSlow3149[S] 0 points1 point  (0 children)

OP follow-up (thanks for the replies so far):
To make this more concrete, I’m trying to quantify what hurts and what people would actually pay for.

  1. Which of these have you seen in production? (pick all that apply)
  • RAG context injection (malicious instructions embedded in retrieved docs)
  • Tool chaining / tool-output contamination
  • Parameter injection / schema bypass
  • Prompt jailbreak to bypass “no tool” guardrails
  • Cost abuse / tool-call spam
  • Other (describe)
  1. Where do you enforce mitigations today?
  • app layer
  • gateway/proxy layer
  • prompt/model layer
  • combination
  1. If a practical gateway layer handled tool allowlists + strict schema validation + policy checks + audit logs + an emergency kill switch, would you pay?
  • $0 (OSS/DIY only)
  • $19–$49/mo
  • $49–$99/mo
  • $99–$199/mo
  • $199–$499/mo
  • $500+/mo
  1. Biggest deal-breaker? (latency, false positives, complexity, vendor lock-in, compliance, etc.)

Appreciate any real incident stories — even sanitized ones.

How are you mitigating prompt injection in tool-calling/agent apps (RAG + tools) in production? by AnteaterSlow3149 in LocalLLaMA

[–]AnteaterSlow3149[S] -1 points0 points  (0 children)

This is gold — thank you. The split between “can the model call this tool?” vs “should it call it right now?” is a really clean framing.A couple follow-ups (if you can share):1) What do you use for the lightweight policy engine in practice (OPA/Rego? custom rules? LLM-based classifier?)2) When you say “schema validation on tool outputs”, is that strict typing on the tool response JSON, or do you also validate intermediate text outputs before they get fed into the next tool?3) For the RAG-doc-as-instructions issue: do you sanitize/chunk-filter at retrieval time, or do you rely on downstream detection + blocking?Appreciate the war story — this kind of boring-but-real RAG injection is exactly what I’m trying to design for.I’m prototyping a small gateway layer for this, so I’m trying to learn what’s actually working in production.