Language models create new value—and new attack surfaces. This guide maps threats to defenses so you can ship fast without leaking data, executing untrusted actions, or hallucinating your way into incidents.
LLM security applies classic security and privacy discipline to language-model apps: control inputs and outputs, isolate retrieval sources, sandbox tools, protect secrets, and log everything you need for investigations and audits. In practice, this looks like a familiar hardening playbook applied to new components—prompt gateways, retrieval layers, tool bridges, and model endpoints—so that policies live outside prompts and every sensitive action leaves a trace you can explain to auditors.
Threats to Expect (Map Before You Ship)
| Threat | What It Looks Like | Why It’s Bad | Controls |
|---|---|---|---|
| Prompt injection | Inputs attempt to override instructions, jailbreak policies, or exfiltrate secrets. | Policy bypass, data leakage, harmful actions. | Input/output filters, instruction grounding, tool gating, allow/deny lists, retrieval isolation. |
| Data loss & PII leakage | Echoing sensitive content or logging secrets. | Compliance exposure; reputational harm. | PII redaction, secrets scanning, encryption, least-privilege tokens, differential logging. |
| Retrieval poisoning | Malicious or outdated documents skew answers. | Wrong outputs with “credible” citations. | Content signing, source allowlists, chunk-level ACLs, freshness & trust scores. |
| Tool abuse | Untrusted calls to file systems, email, or tickets. | Fraud, data loss, spam. | Sandboxing, dry-run/confirm, rate limits, audit trails, human-in-the-loop. |
| Model supply chain | Tampered models, dependencies, or eval sets. | Silent compromise; drift. | Signed artifacts, SBOMs, reproducible builds, checksum verifies, model-card diffs. |
Threat mapping is most useful when it’s tied to your own workflows. Take a customer-support bot with ticketing access: the risky path is an injected message that induces the model to close, forward, or mass-reply. The fix is layered: classify intent before tool use, dry-run actions with a preview, require a human “approve” click for high-impact operations, and record every tool call with user, tenant, and hash of inputs for later review. The same thinking applies to finance or HR assistants—limit blast radius and make approvals explicit.
Reference Architecture (Safe by Default)
Robust LLM security starts with a policy gateway, a retrieval isolator, a tool sandbox, strong identity/secrets, and full-fidelity observability. The gateway externalizes rules from prompts and enforces per-tenant policies. The retrieval layer treats content like a supply chain: only signed, allowlisted sources are eligible, and each chunk carries access controls and freshness scores. The sandbox mediates tools through allowlists, quotas, and dry-runs so that a single prompt can’t send risky commands at scale. Secrets are short-lived and hardware-protected. Observability ties it together with traces you can export to a SIEM.
Controls That Actually Work
Practical LLM security is layered: harden inputs, enforce safe outputs, gate tools, isolate retrieval, and test aggressively in CI. Input hardening includes strict instruction templates, token stripping/escaping, and intent classification before any tool call. Output hardening verifies citations, checks numbers against guardrails, and blocks categories that violate policy. For tools, require previews for write/act operations and rate-limit by user, tenant, and action type. Retrieval isolation rejects unsigned or low-trust sources and attaches citations with scores so reviewers can spot weak evidence quickly.
- Input hardening: system prompts with firm rules; strip or escape dangerous tokens; intent classifiers before tool use.
- Output hardening: content filters, numerical sanity checks, citation verification, and policy red-teaming.
- Guarded tool use: non-interactive “read” tools first; “write/act” tools require approvals.
- Data tiering: deny raw PII/PHI; feed synthesized summaries when possible.
- Evaluation & red-teaming: adversarial test suites (e.g., OWASP LLM Top 10), regression gates in CI.
Two implementation tips save time. First, encode sensitive toggles as off-by-default environment flags so you don’t rely on prompt phrasing to keep a dangerous feature dormant. Second, ship a “shadow mode” where the system proposes actions but can’t execute them; compare shadow suggestions to human behavior and measure false positives/negatives before granting any autonomy.
KPIs & Evidence (So Audits Go Smoothly)
- Prompt-injection block rate; jailbreak false-negative rate—core LLM security indicators.
- PII redaction accuracy; secrets exposure incidents (target: zero).
- Tool-call approval rates; failed dry-runs caught pre-execution.
- Trace coverage and retention controls for audits.
Auditors care about outcomes and evidence. Define severity levels for incidents, SLAs for containment and notification, and a retention window for prompts, responses, tool calls, and model versions. Store hashes of prompts/responses so you can prove integrity without keeping raw text indefinitely. This makes reviews faster and helps privacy teams enforce minimization.
Buyer Checklist (Copy/Paste)
- Do you provide a centralized LLM security gateway with input/output filtering and per-tenant policy?
- How do you isolate retrieval sources (signing, allowlists, freshness, chunk ACLs)?
- Describe your tool sandbox (allowlists, dry-runs, approvals, quotas, egress).
- What secrets and identity practices (short-lived tokens, workload identity, KMS/HSM)?
- What telemetry is exportable (traces, hashes, model versions, user IDs)?
- How do you evaluate (OWASP LLM Top 10, adversarial suites, regression in CI)?
- How do you handle PII/PHI (minimization, redaction, retention, erasure)?
- Incident response playbook (severities, SLAs, customer comms)?
For procurement, insist on demos where the vendor shows blocked prompt injections, rejected poisoned documents, and gated tool calls with human confirmation. Ask for SOC reports, pen-test summaries, and evidence that model and data pipelines are signed end-to-end. These are simple ways to separate slideware from systems you can trust.
Proof & Authoritative Resources
Use recognized frameworks to ground your LLM security program and satisfy audits:
- NIST — AI Risk Management Framework (governance and risk treatment)
- OWASP — Top 10 for LLM Applications (threat catalog & test ideas)
- ENISA — Cybersecurity Publications (data minimization, logging, identity)
- NIST — Secure Software Development Framework (supply-chain and SDLC practices)
- ISO/IEC 27001 (ISMS controls that intersect with LLM security)
Internal Links on Bulktrends
Putting It Together
The best LLM security programs look like strong SaaS security: clear policies, layered controls, and evidence auditors can read. Build the gateway, isolate retrieval, sandbox tools, protect secrets, and trace everything. Then keep testing and patching as models and prompts evolve. Treat the model like middleware, not magic—give it privileges only when necessary, and make those privileges observable.
When leadership asks, “Can we ship this safely?”, answer with a measured plan: a week for gateway policies, two weeks for retrieval isolation, another for tool sandboxing, then continuous red-team tests. That’s what modern LLM security looks like in practice—useful, measurable, and repeatable. Set quarterly goals: reduce tool-call false positives by half, increase trace coverage to 99%, and shorten incident triage by adding better metadata to traces.
Bottom line: adopt LLM security as an engineering habit, not a one-time audit. It will make releases faster, not slower. Ship small, collect evidence, and expand scope only when your controls are boring and your incident reviews are short.
Disclaimer: Controls must match your sector’s legal and privacy requirements. Test with red teams and real users before scaling.
