How do you secure an LLM application’s infrastructure?

Curated by Kory White · Fractional CRO, CRO Syndicate

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📅 Published Jun 27, 2026 · Updated Jun 27, 2026 · 7 min read

How do you secure an LLM application's infrastructure?

Direct Answer

You secure an LLM application's infrastructure by defending every layer between the user and the model: validate and constrain inputs to blunt prompt injection, lock down secrets and model access with least-privilege credentials, sandbox any tools or code the model can call, filter and govern outputs, and put a gateway in front that handles authentication, rate limiting, logging, and PII redaction.

The core mindset is to treat all model inputs and outputs as untrusted — the LLM is a powerful but gullible component, so the surrounding infrastructure must enforce the guarantees the model cannot. The OWASP Top 10 for LLM Applications is the standard checklist for what to defend.

Treat prompt injection as the primary threat

The single biggest difference from traditional app security is prompt injection: an attacker plants instructions in text the model reads — directly in a chat box, or *indirectly* in a web page, document, email, or database row the model later ingests — and the model follows them.

You cannot fully "filter" this away, because to the model, instructions and data look identical. Instead you contain its impact.

Defensive layers include: clearly separating system instructions from user content (using the provider's system/developer role and structured message formats), instructing the model not to follow instructions found in retrieved content, and — most importantly — assuming injection *will* succeed and limiting what a compromised model can do.

If the model can only return text and has no powerful tools, a successful injection is far less damaging than one that can call an API to delete records. Input scanners (Lakera Guard, Prompt Guard from Meta, NVIDIA NeMo Guardrails, Rebuff) add a probabilistic layer, but they are mitigation, not a guarantee.

flowchart LR A[User / retrieved content] --> B[Input validation + injection scanner] B --> C[Gateway: auth, rate limit, redact] C --> D[LLM with system prompt] D --> E{Tool call?} E -->|Yes| F[Sandboxed tool, least privilege] E -->|No| G[Output filter + PII scan] F --> G G --> H[Response + audit log]

Lock down secrets, credentials, and model access

LLM apps accumulate sensitive credentials: provider API keys, vector database tokens, and downstream service secrets. Store them in a dedicated secrets manager (AWS Secrets Manager, HashiCorp Vault, Google Secret Manager, Azure Key Vault) rather than environment files committed to a repo, rotate them on a schedule, and scope each key to the minimum it needs.

Put model access behind a gateway or proxy (LiteLLM, Cloudflare AI Gateway, Kong AI Gateway, Portkey) so individual app components never hold raw provider keys directly — the gateway holds the key and enforces per-team budgets, rate limits, and logging.

Apply least privilege everywhere. The service account that runs the LLM app should not have broad database or cloud permissions; it should only reach the specific resources the application needs. If you self-host models, isolate the inference servers in their own network segment, restrict inbound traffic, and keep the runtime and dependencies patched — model-serving stacks (vLLM, Triton, TGI) are software with their own CVEs.

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Sandbox tools, code execution, and agents

The most dangerous LLM deployments are agentic: the model decides which tools to call, and some of those tools execute code, run SQL, browse the web, or hit internal APIs. This is where prompt injection turns into real damage (OWASP calls the risk "excessive agency"). Every tool the model can invoke must enforce its own authorization independent of the model's intent.

Concretely: run any model-generated code in an isolated sandbox (gVisor, Firecracker microVMs, Docker with dropped capabilities, or a hosted sandbox like E2B) with no network access unless explicitly required. Parameterize and allow-list database queries rather than letting the model write raw SQL against a privileged connection.

Require human approval for high-impact actions (sending money, deleting data, emailing customers). Give each tool the narrowest scope possible, and log every tool invocation with its arguments for audit.

Govern inputs and outputs at the boundary

Wrap the model with guardrails on both sides. On input: enforce length limits and schemas, strip or escape control characters, and scan for injection and policy violations. On output: scan for leaked secrets, PII, toxic content, and hallucinated links before the response reaches the user or a downstream system.

Tools like NVIDIA NeMo Guardrails, Guardrails AI, Microsoft Presidio (for PII detection/redaction), and Lakera Guard provide programmable rails for this.

Pay special attention to data leakage. In RAG systems, enforce access control *at retrieval time* — a user should only retrieve chunks they are authorized to see, or the model will happily summarize someone else's confidential document. Filter retrieved context by the requesting user's permissions, and never put data into the prompt that the user is not entitled to.

Mask PII before it ever reaches a third-party model API if your compliance regime requires it.

flowchart TD A[Incoming request] --> B{Authenticated?} B -->|No| Z[Reject] B -->|Yes| C[Rate limit + budget check] C --> D[Redact PII / scan input] D --> E[Permission-filtered retrieval] E --> F[LLM call via gateway] F --> G[Output guardrails: PII, secrets, toxicity] G --> H[Log full trace] H --> I[Return response]

Monitor, log, and rate-limit everything

LLM endpoints are expensive and abusable, so observability is a security control, not just an ops nicety. Enforce rate limits and token/cost quotas per user and per API key to blunt denial-of-wallet attacks, where an attacker drives up your bill with floods of expensive requests.

Log every request and response with a trace ID, the model and version used, token counts, latency, and any guardrail triggers — using LLM observability platforms (Langfuse, Arize Phoenix, Helicone, Datadog LLM Observability) or your existing SIEM. These logs are what let you detect abuse, investigate incidents, and prove compliance.

Treat the model supply chain as part of your attack surface too: pin model versions, verify checksums of downloaded open-weight models, and vet third-party plugins, MCP servers, and tools before granting them access. A poisoned model or a malicious plugin is an infrastructure compromise.

Build security into the deployment lifecycle

Securing the running system is only half the job; the pipeline that ships it matters just as much. Run adversarial testing (red-teaming) before launch and on a schedule afterward — tools like Microsoft PyRIT, Garak, and Lakera's red-team offerings probe your app with known injection patterns, jailbreaks, and data-exfiltration prompts so you find weaknesses before attackers do.

Treat the prompts themselves as versioned, reviewed artifacts: a change to a system prompt can silently remove a safety constraint, so it belongs in source control with the same review gates as code.

Harden the infrastructure underneath the model with standard cloud security hygiene. Keep inference servers, vector databases, and orchestration frameworks (LangChain, LlamaIndex, and their dependencies) patched, since these libraries have had real CVEs. Use network policies to restrict which services can reach the model endpoint, encrypt data in transit and at rest, and isolate development, staging, and production keys so a leaked test credential cannot touch production data.

Finally, define an incident-response plan specific to AI failures — what to do when the model leaks data, when a guardrail is bypassed, or when costs spike from abuse — and rehearse it. Security for LLM apps is continuous: the threat landscape, model behavior, and your own prompts all change over time, so revisit the OWASP and NIST checklists each release rather than treating the work as one-and-done.

Sources

OWASP Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
Google Secure AI Framework (SAIF): https://safety.google/cybersecurity-advancements/saif/
Microsoft Presidio (PII detection and anonymization): https://microsoft.github.io/presidio/
NVIDIA NeMo Guardrails: https://github.com/NVIDIA/NeMo-Guardrails
Lakera — prompt injection and LLM security: https://www.lakera.ai/
OWASP GenAI Security Project (LLM & agentic threats): https://genai.owasp.org/

Frequently Asked Questions

What is prompt injection and why can't I just filter it out? Prompt injection is when an attacker plants instructions in text the model reads, causing it to ignore its original task. You cannot fully filter it because the model has no reliable way to distinguish instructions from data — they are all just tokens.

Defense focuses on containing impact: limit the model's tools and permissions so a successful injection cannot cause serious harm.

What is the difference between direct and indirect prompt injection? Direct injection is when the user types malicious instructions into the chat. Indirect injection hides instructions in external content the model later ingests — a web page, PDF, email, or database row pulled into a RAG context.

Indirect injection is more dangerous because the victim user is unaware; defend it by sanitizing and distrusting all retrieved content.

How do I stop an LLM agent from doing damage? Apply least privilege and sandboxing. Give each tool the narrowest scope, run model-generated code in an isolated sandbox with no network by default, require human approval for high-impact actions, and ensure every tool enforces its own authorization regardless of what the model "intends." Assume the model can be tricked and design so that compromise is contained.

Should I send PII to a third-party LLM API? Only if your contracts and compliance regime allow it and the data is necessary. Otherwise redact or tokenize PII before the call using a tool like Microsoft Presidio, or self-host a model so data never leaves your environment. Always know your provider's data-retention and training-use terms, and enforce permission-filtered retrieval so users never see data they shouldn't.

What is a denial-of-wallet attack? It is an abuse where an attacker floods your LLM endpoint with expensive requests to run up your usage bill or exhaust your provider quota, rather than to steal data. Defend it with per-user and per-key rate limits, token/cost budgets enforced at a gateway, authentication on all endpoints, and alerting on unusual spend.

What standards should I follow for LLM security? Start with the OWASP Top 10 for LLM Applications, which enumerates the key risks (prompt injection, insecure output handling, excessive agency, sensitive information disclosure, and more). Pair it with the NIST AI Risk Management Framework and Google's Secure AI Framework (SAIF) for governance, and map controls to your existing security program rather than treating AI as a silo.

Keep reading

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# How do you secure an LLM application's infrastructure?

### Direct Answer
You secure an LLM application's infrastructure by defending every layer between the user and the model: validate and constrain inputs to blunt prompt injection, lock down secrets and model access with least-privilege credentials, sandbox any tools or code the model can call, filter and govern outputs, and put a gateway in front that handles authentication, rate limiting, logging, and PII redaction. The core mindset is to treat all model inputs and outputs as **untrusted** — the LLM is a powerful but gullible component, so the surrounding infrastructure must enforce the guarantees the model cannot. The OWASP Top 10 for LLM Applications is the standard checklist for what to defend.

## Treat prompt injection as the primary threat

The single biggest difference from traditional app security is **prompt injection**: an attacker plants instructions in text the model reads — directly in a chat box, or *indirectly* in a web page, document, email, or database row the model later ingests — and the model follows them. You cannot fully "filter" this away, because to the model, instructions and data look identical. Instead you contain its impact.

Defensive layers include: clearly separating system instructions from user content (using the provider's system/developer role and structured message formats), instructing the model not to follow instructions found in retrieved content, and — most importantly — assuming injection *will* succeed and limiting what a compromised model can do. If the model can only return text and has no powerful tools, a successful injection is far less damaging than one that can call an API to delete records. Input scanners (Lakera Guard, Prompt Guard from Meta, NVIDIA NeMo Guardrails, Rebuff) add a probabilistic layer, but they are mitigation, not a guarantee.

```mermaid
flowchart LR
    A[User / retrieved content] --> B[Input validation + injection scanner]
    B --> C[Gateway: auth, rate limit, redact]
    C --> D[LLM with system prompt]
    D --> E{Tool call?}
    E -->|Yes| F[Sandboxed tool, least privilege]
    E -->|No| G[Output filter + PII scan]
    F --> G
    G --> H[Response + audit log]
```

## Lock down secrets, credentials, and model access

LLM apps accumulate sensitive credentials: provider API keys, vector database tokens, and downstream service secrets. Store them in a dedicated secrets manager (AWS Secrets Manager, HashiCorp Vault, Google Secret Manager, Azure Key Vault) rather than environment files committed to a repo, rotate them on a schedule, and scope each key to the minimum it needs. Put model access behind a **gateway or proxy** (LiteLLM, Cloudflare AI Gateway, Kong AI Gateway, Portkey) so individual app components never hold raw provider keys directly — the gateway holds the key and enforces per-team budgets, rate limits, and logging.

Apply **least privilege** everywhere. The service account that runs the LLM app should not have broad database or cloud permissions; it should only reach the specific resources the application needs. If you self-host models, isolate the inference servers in their own network segment, restrict inbound traffic, and keep the runtime and dependencies patched — model-serving stacks (vLLM, Triton, TGI) are software with their own CVEs.


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## Sandbox tools, code execution, and agents

The most dangerous LLM deployments are **agentic**: the model decides which tools to call, and some of those tools execute code, run SQL, browse the web, or hit internal APIs. This is where prompt injection turns into real damage (OWASP calls the risk "excessive agency"). Every tool the model can invoke must enforce its own authorization independent of the model's intent.

Concretely: run any model-generated code in an isolated sandbox (gVisor, Firecracker microVMs, Docker with dropped capabilities, or a hosted sandbox like E2B) with no network access unless explicitly required. Parameterize and allow-list database queries rather than letting the model write raw SQL against a privileged connection. Require human approval for high-impact actions (sending money, deleting data, emailing customers). Give each tool the narrowest scope possible, and log every tool invocation with its arguments for audit.

## Govern inputs and outputs at the boundary

Wrap the model with guardrails on both sides. On **input**: enforce length limits and schemas, strip or escape control characters, and scan for injection and policy violations. On **output**: scan for leaked secrets, PII, toxic content, and hallucinated links before the response reaches the user or a downstream system. Tools like NVIDIA NeMo Guardrails, Guardrails AI, Microsoft Presidio (for PII detection/redaction), and Lakera Guard provide programmable rails for this.

Pay special attention to **data leakage**. In RAG systems, enforce access control *at retrieval time* — a user should only retrieve chunks they are authorized to see, or the model will happily summarize someone else's confidential document. Filter retrieved context by the requesting user's permissions, and never put data into the prompt that the user is not entitled to. Mask PII before it ever reaches a third-party model API if your compliance regime requires it.

```mermaid
flowchart TD
    A[Incoming request] --> B{Authenticated?}
    B -->|No| Z[Reject]
    B -->|Yes| C[Rate limit + budget check]
    C --> D[Redact PII / scan input]
    D --> E[Permission-filtered retrieval]
    E --> F[LLM call via gateway]
    F --> G[Output guardrails: PII, secrets, toxicity]
    G --> H[Log full trace]
    H --> I[Return response]
```

## Monitor, log, and rate-limit everything

LLM endpoints are expensive and abusable, so observability is a security control, not just an ops nicety. Enforce **rate limits and token/cost quotas** per user and per API key to blunt denial-of-wallet attacks, where an attacker drives up your bill with floods of expensive requests. Log every request and response with a trace ID, the model and version used, token counts, latency, and any guardrail triggers — using LLM observability platforms (Langfuse, Arize Phoenix, Helicone, Datadog LLM Observability) or your existing SIEM. These logs are what let you detect abuse, investigate incidents, and prove compliance.

Treat the model **supply chain** as part of your attack surface too: pin model versions, verify checksums of downloaded open-weight models, and vet third-party plugins, MCP servers, and tools before granting them access. A poisoned model or a malicious plugin is an infrastructure compromise.

## Build security into the deployment lifecycle

Securing the running system is only half the job; the pipeline that ships it matters just as much. Run **adversarial testing (red-teaming)** before launch and on a schedule afterward — tools like Microsoft PyRIT, Garak, and Lakera's red-team offerings probe your app with known injection patterns, jailbreaks, and data-exfiltration prompts so you find weaknesses before attackers do. Treat the prompts themselves as versioned, reviewed artifacts: a change to a system prompt can silently remove a safety constraint, so it belongs in source control with the same review gates as code.

Harden the infrastructure underneath the model with standard cloud security hygiene. Keep inference servers, vector databases, and orchestration frameworks (LangChain, LlamaIndex, and their dependencies) patched, since these libraries have had real CVEs. Use network policies to restrict which services can reach the model endpoint, encrypt data in transit and at rest, and isolate development, staging, and production keys so a leaked test credential cannot touch production data. Finally, define an incident-response plan specific to AI failures — what to do when the model leaks data, when a guardrail is bypassed, or when costs spike from abuse — and rehearse it. Security for LLM apps is continuous: the threat landscape, model behavior, and your own prompts all change over time, so revisit the OWASP and NIST checklists each release rather than treating the work as one-and-done.

## Sources
- OWASP Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
- Google Secure AI Framework (SAIF): https://safety.google/cybersecurity-advancements/saif/
- Microsoft Presidio (PII detection and anonymization): https://microsoft.github.io/presidio/
- NVIDIA NeMo Guardrails: https://github.com/NVIDIA/NeMo-Guardrails
- Lakera — prompt injection and LLM security: https://www.lakera.ai/
- OWASP GenAI Security Project (LLM & agentic threats): https://genai.owasp.org/

## Frequently Asked Questions

**What is prompt injection and why can't I just filter it out?**
Prompt injection is when an attacker plants instructions in text the model reads, causing it to ignore its original task. You cannot fully filter it because the model has no reliable way to distinguish instructions from data — they are all just tokens. Defense focuses on containing impact: limit the model's tools and permissions so a successful injection cannot cause serious harm.

**What is the difference between direct and indirect prompt injection?**
Direct injection is when the user types malicious instructions into the chat. Indirect injection hides instructions in external content the model later ingests — a web page, PDF, email, or database row pulled into a RAG context. Indirect injection is more dangerous because the victim user is unaware; defend it by sanitizing and distrusting all retrieved content.

**How do I stop an LLM agent from doing damage?**
Apply least privilege and sandboxing. Give each tool the narrowest scope, run model-generated code in an isolated sandbox with no network by default, require human approval for high-impact actions, and ensure every tool enforces its own authorization regardless of what the model "intends." Assume the model can be tricked and design so that compromise is contained.

**Should I send PII to a third-party LLM API?**
Only if your contracts and compliance regime allow it and the data is necessary. Otherwise redact or tokenize PII before the call using a tool like Microsoft Presidio, or self-host a model so data never leaves your environment. Always know your provider's data-retention and training-use terms, and enforce permission-filtered retrieval so users never see data they shouldn't.

**What is a denial-of-wallet attack?**
It is an abuse where an attacker floods your LLM endpoint with expensive requests to run up your usage bill or exhaust your provider quota, rather than to steal data. Defend it with per-user and per-key rate limits, token/cost budgets enforced at a gateway, authentication on all endpoints, and alerting on unusual spend.

**What standards should I follow for LLM security?**
Start with the OWASP Top 10 for LLM Applications, which enumerates the key risks (prompt injection, insecure output handling, excessive agency, sensitive information disclosure, and more). Pair it with the NIST AI Risk Management Framework and Google's Secure AI Framework (SAIF) for governance, and map controls to your existing security program rather than treating AI as a silo.

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