How to operationalize the OWASP LLM top 10 and (actually) secure GenAI apps

Security folks love a good framework. It’s comforting, familiar. Nice, neat boxes, numbered risks, and the sweet illusion that if you “adopt” something, you’ve magically reduced your exposure. Sounds great, right?

But anyone who’s shipped a GenAI-powered feature into production knows the truth: the OWASP Top 10 for LLMs isn’t a checklist. It’s the red pill. 

Choose it, and you’ll see the chaotic, dynamic, failure-prone reality beneath. And you either build controls that either help you weather the storm of real attackers, or see you dragged under by prompt chains, compromised embeddings, and over-permissioned agents trying their best to wreck your infrastructure.

This post is about the part everyone avoids: how to operationalize the OWASP Top 10 so it survives real workloads, not just compliance slides. Read on for the technical patterns that security teams and CISOs should enforce if they want to deploy LLM apps without rolling dice every time a user types something weird.

1. Frameworks don’t guarantee security

The OWASP LLM Top 10 is clear on one thing: LLM risks are lurking all around it. Ingestion. Training. RAG pipelines. System prompts. Outputs. Runtime. Even billing. It’s not a “runtime-only” issue and it’s not a “trust and safety” situation. It’s an application security issue. 

The main difference is that the ‘application’ in this case is a stochastic generator (i.e. a system where outputs vary because the model is based on probability). Because that generator is wired into multiple tools, it’s secured in the same way you would any app that can trigger downstream actions: control inputs, outputs, and permissions, and don’t get distracted by the probabilistic gloss.

TL;DR: If you treat the OWASP list like a policy doc, you’ll lose. If you treat it like an engineering spec, you’ll win.

Every risk needs an implemented control. Every control needs validation. Every validation needs monitoring. Anything less is window dressing. Let’s see what that looks like in action.

2. Recommendations: Converting OWASP risks into actionable control

This section will take you through the minimum viable engineering realities if you want to avoid being the CISO who explains to the board why your chatbot leaked system prompts or issued PowerShell commands in a customer-facing workflow.

LLM01: Prompt injection → Input/Output verification layer

Prompt injection is table stakes now. Attackers use nested queries, multi-hop instructions, base64-wrapped payloads, subtle re-framing, or poisoning in your own context window.

Some controls that work:

• Input scanning for jailbreak patterns, obfuscated intent, encoding tricks, and indirect injections referencing external content.

• Output sanitization before downstream systems act on the model’s response.

• Strict separation of “reasoning text” from “action instructions” if your model calls tools, APIs, or executes actions.

• Adversarial evaluation in CI/CD for every model update, prompt update, or retrieval logic change.

TL;DR: Fail to validate both directions, and you’re not protected.

LLM02: Sensitive information disclosure → Multilingual data-leak defense

Models leak. System prompts leak. PII leaks. Embeddings leak. It’s not hypothetical; it’s mechanical. Security teams need:

• PII detectors covering multilingual inputs and outputs (attackers switch languages to avoid filters).

• Explicit guardrails that block proprietary data, internal project names, or ID formats.

• Automated evaluation runs that stress-test leakage paths during development.

TL;DR: Don’t consider an LLM should “safe” unless it has outbound content filters with actual teeth.

LLM03: Supply Chain Vulnerabilities → Verification and Integrity, Not Blind Trust

Models pull in dependencies, embeddings, weights, fine-tuning datasets, and plugins. You’ll need:

• Dataset provenance checks.

• Dependency scanning for any plugin, tool, or API your agent can call.

• Model integrity validation (signing or hash verification).

• Red team testing to surface suspicious behaviors from tampered or poisoned components.

TL;DR: LLM supply chain is every nightmare from the last decade smashed together. Treat it accordingly.

LLM04: Data and model poisoning → Backdoor detection before deploy

News flash: poisoning isn’t some fringe lab problem anymore. It shows up as behavioral quirks that only activate under crafted prompts. That calls for:

• Poisoning-aware evaluation suites in CI.

• Trigger phrase scanning across large-scale test sets.

• Behavioral drift detection between model versions.

TL;DR: If your ML team can’t describe their poisoning mitigation strategy in one sentence, you’re unprotected.

LLM05: Improper Output Handling → Hard Boundaries Between Model and System

This is where security teams get blindsided. LLMs put out:

• Shell commands

• SQL queries

• HTML/JS payloads

• Cloud CLI instructions

• API calls that look perfectly legit

• Social engineering hooks shaped as “user messages”

Controls:

• Output sanitization gates before anything touches a database, tool, API, or workflow.

• No direct execution of LLM-generated code or commands without validation.

• Context-aware allowlists for what the model is permitted to output.

TL;DR: This OWASP category is where most real-world incidents are quietly happening.

LLM06: Excessive agency → Real permissioning, not vibes

Agentic systems are cool until your model decides to “optimize” something in production.

Technical defenses:

• Granular tool scopes.

• Command wrappers with strict schemas.

• Explicit preconditions that must be satisfied for tool invocation.

• Denied-by-default agent permissions.

TL;DR: An agent with broad permissions is not a product feature; It is an incident in which the exploited vulnerability waits to be assigned a CVE ID.

LLM07: System Prompt Leakage → Obfuscation + Layered Controls

Attackers love extracting system prompts because those prompts reveal rules, logic, APIs, keys, or internal reasoning.

You need:

• Leakage detection for prompt-extraction attempts.

• Multi-layer prompts where sensitive parts never touch the model context.

• Adversarial red teaming focused on indirect extraction techniques.

TL;DR: If your system prompt can be leaked, assume it will be at some point. 

LLM08: Vector and embedding weaknesses → RAG segmentation and access controls

RAG pipelines create massive unintended access surfaces. Defenses to consider include:

• Namespace segmentation for embeddings.

• Query-level access controls so the model can’t retrieve data it shouldn’t.

• Leakage detection for embedding content.

• Per-document security classifications in your vector store.

TL;DR: Treat your vector DB like an internal data lake with compliance exposure, because that’s exactly what it is.

LLM09: Misinformation and hallucinations → Factuality and output validation

No CISO wants to explain that their LLM “just made up” regulatory requirements. Some controls you can put in place include:

• Output factuality checks for high-risk workflows.

• Bias monitoring across model versions.

• Structured response formats to reduce hallucination space.

TL;DR: “LLMs hallucinate” isn’t an excuse; it’s a control surface.

LLM10: Unbounded consumption → Hard limits everywhere

Attackers hammer LLM endpoints to:

• Spike costs.

• Force DoS.

• Perform extraction attacks.

• Induce degraded behavior.

You need:

• Strict rate limiting at model and gateway levels.

• Session-level resource accounting.

• Anomaly detection for repetitive, extraction-like patterns.

• Exfiltration-aware logging.

TL;DR: This is the LLM equivalent of someone running rm -rf / on your production system.

3. Build LLM security like you expect to be attacked

Here’s the blueprint security teams should force into the roadmap, non-negotiable:

• Adversarial evaluation baked into CI: Every prompt change, dataset update, model upgrade, or RAG logic change triggers adversarial tests.

• Runtime input/output filtration layer: A must-have. This is your new WAF, except the stakes are bigger.

• Strict permission boundaries for agents and tools: If your agent can call it, you must log it, constrain it, and verify it.

• Output handling = security control, not dev concern: Sanitize all of it. Everything. No exceptions for “trusted users.”

• Complete telemetry across the model boundary: You can’t defend what you can’t see. Capture: Inputs, outputs, intermediate context, tool calls, RAG retrievals, and consumption patterns.

• Red team everything: Agents. Prompts. Guardrails. Retrieval paths. Tool integrations. Output handling. All of it. If it can be attacked, assume someone is trying.

4. The CISO download

If you want something to hand to an exec, here it is:

• The OWASP Top 10 for LLMs is only useful if each risk becomes an implemented control.

• LLM security requires both pre-deployment adversarial testing and runtime protections.

• Prompt injection, output handling, RAG data exposure, and agent permissions are where real incidents are happening.

• Security teams must own observability, enforcement, and red teaming.

• The organizations treating LLM security as engineering reality, not policy fiction, are the ones who aren’t going to get blindsided.

What’s next for the OWASP Top 10?

The OWASP Top 10 for LLMs isn’t the finish line. It’s the map of a battlefield your organization is already standing in. The teams that stay on track are the ones treating GenAI systems like high-risk, high-privilege software components, not shiny toys with a safety disclaimer.

Operationalizing this framework means building controls that assume attackers are experimenting faster than you’re deploying. It means hardening prompts, validating outputs, constraining agents, instrumenting the entire LLM pipeline, and red teaming every change like it’s a breaking update to a core service. None of this is optional anymore.

CHECK OUT OUR AI RED TEAMER PATH

The ones who get it right will be running circles around those still fooling themselves with the notion that “we read the OWASP doc” counts as security. The red pill is more uncomfortable, yes, but it’s the only version of reality you can build a truly defensible architecture on.