9 Best Prompt Injection Detection Tools in 2026 for AI Agents

Last updated October 25, 2026. Detection scope, tool output coverage, languages, and pricing were checked against vendor docs, pricing pages, and model cards on that date.

The best prompt injection detection tools for AI agents in 2026 are Lakera Guard, Azure Prompt Shields, Amazon Bedrock Guardrails, Google Model Armor, Prisma AIRS, CrowdStrike Falcon AIDR, Llama Prompt Guard 2, NVIDIA NeMo Guardrails, and PIGuard. Lakera Guard is the strongest default because it documents screening of tool responses and MCP tool descriptions, which is where agent attacks now land.

Prompt injection detection means classifying text before your model or agent acts on it, so hidden instructions in a user message, web page, email, or tool result get flagged or blocked. Agents raise the stakes because one poisoned tool output can trigger real actions. Our agent security coverage sits inside the wider AI governance and security hub.

We read every vendor doc and pricing page on October 25, 2026, then ran three open classifiers on CPU across 505 samples, including a 50-item agent tool-output corpus we wrote for this guide. No vendor paid for placement or reviewed this page, per our editorial standards and disclosure policy.

Prompt Injection Detection Tools at a Glance

Pick Lakera Guard for agent tool outputs across any cloud, your cloud provider shield if you run on Azure, AWS, or Google Cloud, Prisma AIRS or Falcon AIDR if those platforms already run your SOC, and Prompt Guard 2, NeMo, or PIGuard for self-hosted control. The table summarizes each tool from pages checked on October 25, 2026.

Tool	How it runs	Agent tool output	Price signal (October 2026)
Lakera Guard (Check Point)	SaaS API or self-hosted	Yes, via tool role and MCP description screening	Free 10,000 requests a month; Enterprise quote
Azure Prompt Shields	Azure API and Foundry guardrails	Preview for supported Foundry tools	Free 5,000 text records a month; Standard per 1,000 records
Amazon Bedrock Guardrails	Bedrock guardrail or InvokeGuardrailChecks	Not on Converse tool results; call checks yourself	$0.08 per 1,000 text units via InvokeGuardrailChecks
Google Model Armor	Google Cloud API, templates	Any text you send; single turn	2M tokens free, then $0.10 per 1M tokens
Prisma AIRS	API intercept or network intercept	Any text you send to the scan API	Palo Alto Networks quote
CrowdStrike Falcon AIDR	Collectors with policy rules	Collector traffic; MCP validation detector	Quote; AIDR moves into Falcon Guardian
Llama Prompt Guard 2	Open-weight classifier	Any text you send, 512 tokens	Free under the Llama license
NVIDIA NeMo Guardrails	Open source rails framework	Injection rules on bot output and tool calls you route	Free, Apache-2.0
PIGuard	Open-weight classifier	Any text you send, 512 tokens	Free, MIT

Verdict: Managed shields from your cloud provider are the fastest start, but only Lakera and Azure document tool response screening as a product feature. Everyone else needs your code to send tool output to the detector before the agent reads it.

What Does Prompt Injection Detection Actually Do?

A prompt injection detector scores text for instructions that try to override the system prompt or redirect an agent, then returns a verdict your app can log, block, or route to a human. Indirect attacks hide in content the agent reads, such as emails, web pages, and tool results, and they pose the bigger risk for agents with write access.

Most products here are classifiers behind an API or open weights you host yourself. They do not stop an attack on their own; your agent loop has to call them at each point where untrusted text enters. Our MCP security tools guide covers the tool-server side of the same problem, and our ToFu MCP bug study shows how often tool metadata goes wrong.

• User input: screen the prompt before the model sees it.
• Retrieved content: screen documents, pages, and emails pulled by RAG or browsing tools.
• Tool results and descriptions: screen MCP tool output and new tool metadata before the agent plans its next step.

How We Picked These Prompt Injection Detection Tools

We started from products that document a prompt injection or prompt attack detector on their own pages, then scored each on agent coverage, deployment options, and how honest the docs are about limits. Every claim below comes from a vendor doc, pricing page, model card, or paper, all opened on October 25, 2026.

• Agent coverage: whether the detector documents tool output, tool description, or MCP screening.
• Evidence quality: published metrics, documented false positive behavior, and our own CPU test where weights are public.
• Deployment: managed API, cloud guardrail, self-hosted container, or open weights.
• Languages and limits: documented language coverage, token windows, and encoding gaps.
• Cost signal: free tiers and public per-unit prices where vendors publish them.

What Our October 25 Classifier Test Found

PIGuard was the most balanced open classifier in our test, catching 72% of injected agent tool outputs while flagging 12% of NotInject benign prompts and 24% of benign tool outputs. ProtectAI’s v2 model caught 52% of agent injections and flagged 43% of NotInject prompts. deepset’s model flagged almost everything.

We ran each model at a 0.5 threshold over the deepset prompt-injections test split (116 items), the 339 benign trigger-word prompts in NotInject, and 50 agent tool outputs we wrote: 25 with hidden instructions and 25 benign twins in the same formats. Formats include emails, GitHub issues, READMEs, MCP results, CSV cells, calendar notes, and MCP tool descriptions.

$ python bench.py   # CPU, 4 threads, max 512 tokens, threshold 0.5
datasets: deepset_test=116  notinject=339  agent_corpus=50
model                       deepset  agent_inj  notinject_fp  agent_fp  p50
protectai v2 (deberta)       37%       52%        43%          28%     57 ms
PIGuard (deberta)            67%       72%        12%          24%     58 ms
deepset (deberta)            98%      100%        71%          96%     58 ms

Both balanced models missed our poisoned MCP tool description, a hidden discount change in a CRM note, and a shopping instruction buried in a product review. Benign developer text such as a code comment saying to ignore leading whitespace tripped both. Median latency stayed near 58 ms per sample on CPU for all three.

What this means for buyers: treat any single score as a lead and test detectors on your own tool outputs before trusting them. Ask managed vendors for false positive rates on benign developer and business text, since that is where open models struggled most.

1. Lakera Guard: Best Overall for Agent Tool Outputs

Lakera Guard is the safest default for agent builders because its docs tell you to pass tool responses with a tool role and to screen tool descriptions when you add a new MCP server. The prompt defense docs, now branded Check Point AI Guardrails, cover direct attacks, jailbreaks, and indirect attacks in documents and tool output.

Lakera offers Detect mode for logging and Enforce mode for blocking, plus an Agent Behavior Defense layer for multi-step agents. The docs claim screening in over 100 languages and scripts, which matters if your agents read customer email or global web content.

• Best for: Teams on any cloud that want one API to screen user prompts, retrieved documents, tool results, and MCP tool metadata.
• Covers: Direct and indirect prompt attacks, jailbreaks, tool output, tool descriptions, 100+ languages.
• Pricing (October 2026): Community plan free for 10,000 requests a month with an 8,000-token prompt cap; Enterprise quote adds self-hosting, SSO, and SIEM.
• Watch out for: The Community tier is SaaS only with EU data residency; self-hosting and version pinning need Enterprise.

2. Azure AI Content Safety Prompt Shields: Best for Azure and Foundry Agents

Prompt Shields fits teams building on Azure OpenAI or Foundry Agent Service because user prompt attack detection is on by default and document attack detection can scan tool responses for supported tools in preview. The Prompt Shields docs split detection into user prompt attacks and document attacks hidden in emails, pages, and files.

Document attack detection is off by default in Foundry, so turn it on for agents. Microsoft says both models are primarily trained and evaluated on English, so test every language your agents read. Spotlighting, which marks untrusted document text for the model, is a separate toggle.

• Best for: Azure OpenAI and Foundry Agent Service builders who want a native guardrail with no new vendor.
• Covers: User prompt attacks, document attacks, tool response scanning for supported Foundry tools in preview.
• Pricing (October 2026): Free tier of 5,000 text records a month; Standard bills per 1,000 text records of up to 1,000 characters; disconnected containers on commitment tiers.
• Watch out for: Mostly English training data, and tool response scanning is still preview.

3. Amazon Bedrock Guardrails: Best for Apps Already on Bedrock

Bedrock Guardrails is the natural pick for Bedrock apps, but its docs state that the prompt attack filter does not evaluate tool results or tool definitions, so agent builders must screen tool output with a separate call. The prompt attack docs cover jailbreaks, prompt injection, and prompt leakage on the Standard tier.

With InvokeModel, only text inside guardrail input tags gets checked, so an untagged prompt is not filtered. The InvokeGuardrailChecks API runs prompt attack as its own detect-only check that you can call after a tool returns. Filter strength runs from NONE to HIGH, with BLOCK or detect actions.

• Best for: Teams on Bedrock models that want guardrails in the same console and IAM model as inference.
• Covers: Jailbreaks, prompt injection, prompt leakage (Standard tier), configurable strength per guardrail.
• Pricing (October 2026): Bedrock pricing: $0.08 per 1,000 text units for prompt attack via InvokeGuardrailChecks, or $0.15 per 1,000 as part of content filters.
• Watch out for: Tool results and tool specs are skipped on Converse, and missing input tags silently disable the filter on InvokeModel.

4. Google Cloud Model Armor: Best for Google Cloud and Low Unit Cost

Model Armor is the low-cost choice for Google Cloud teams, with 2 million free tokens a month standalone and $0.10 per million after that, plus a prompt injection and jailbreak filter with three confidence levels. The Model Armor overview lists HIGH, MEDIUM_AND_ABOVE, and LOW_AND_ABOVE thresholds and recommends Medium as the starting point.

Model Armor screens prompts, responses, and documents up to 4 MB, and templates can inspect only or inspect and block. The docs list clear limits: inputs under three words return no match, Base64, hex, and URL-encoded text is not decoded, and screening is single turn. Prompt injection testing covers nine languages.

• Best for: Vertex AI and Gemini teams that want a per-token detector inside Security Command Center.
• Covers: Prompt injection and jailbreak, responsible AI filters, malicious URLs, sensitive data, document screening.
• Pricing (October 2026): Standalone pricing 2 million tokens free per month, then $0.10 per million tokens; larger allowances inside Security Command Center tiers.
• Watch out for: No decoding of encoded payloads and no multi-turn context, so pair it with your own normalization step.

5. Palo Alto Networks Prisma AIRS: Best for Palo Alto Security Stacks

Prisma AIRS suits teams that already run Palo Alto Networks firewalls or Cortex and want prompt injection verdicts from the same security profile that handles data loss and malicious URLs. The API security profile docs show prompt injection detection set to allow or block per profile.

Apps call a synchronous scan endpoint and get back a verdict such as injection true with a block action. Documented prompt injection languages are English, Spanish, Russian, German, French, Japanese, Portuguese, Italian, and Simplified Chinese. The same profile can also check toxic content and sensitive data in one request.

• Best for: Palo Alto Networks customers who want API and network intercept modes under one policy.
• Covers: Prompt injection, toxic content, malicious URLs, sensitive data, model responses.
• Pricing (October 2026): Quote through Palo Alto Networks; no public per-call price on the pages we checked.
• Watch out for: You must send tool output to the scan API yourself; the docs we read do not describe automatic tool result hooks.

6. CrowdStrike Falcon AIDR: Best for Falcon Shops That Want Custom Examples

Falcon AIDR fits CrowdStrike customers who want a Malicious Prompt detector that learns from their own benign and malicious examples, inside the same console as endpoint telemetry. The prompt rules docs let you set the detector to report or block and add example prompts to cut false positives.

Collectors sit in front of AI apps and agents, and the docs list separate detectors for MCP validation and hidden or invisible characters. A banner on the docs says AIDR reaches end of support on November 30, 2026, with its features continuing in Falcon Guardian, so buy with that roadmap in view.

• Best for: Falcon customers who want prompt detection tied to endpoint and identity telemetry.
• Covers: Malicious prompts, prompt injection, jailbreaks, MCP validation, invisible character tricks, custom examples.
• Pricing (October 2026): Quote through CrowdStrike; capabilities move into Falcon Guardian.
• Watch out for: Product naming is changing; confirm the Guardian SKU and migration path before signing a multi-year term.

7. Meta Llama Prompt Guard 2: Best Open-Weight Classifier With Published Metrics

Prompt Guard 2 is the open-weight pick for teams that want to host their own detector, with an 86M model that Meta reports at 97.5% recall at 1% false positive rate on its English evaluation set. The model card also lists a 22M version for lower latency at 88.7% recall on the same measure.

Meta reports 92.4 ms for the 86M model and 19.3 ms for the 22M model on an A100 at 512 tokens. On AgentDojo, Meta reports the 86M model prevented 81.2% of attacks at a 3% utility cost, against 22.2% for a ProtectAI model in the same table. The weights sit behind a gated Hugging Face page, so we could not run them on our corpus without accepting the Llama license.

• Best for: Platform teams that want a self-hosted classifier with vendor-published agent metrics.
• Covers: Binary benign or malicious labels for prompt injection and jailbreaks, multilingual 86M model.
• Pricing (October 2026): Free under the Llama 4 Community License; you pay for hosting.
• Watch out for: 512-token window means long tool outputs need chunking, and the figures above are Meta’s own numbers.

8. NVIDIA NeMo Guardrails: Best Open Framework for Layered Rails

NeMo Guardrails fits teams that want an open source rails layer around their agent, combining jailbreak heuristics, an optional NVIDIA NIM jailbreak classifier, and YARA-based injection rules in one config. The jailbreak protection docs publish their own trade-offs, which is rare.

NVIDIA says its length-based heuristic catches 31.19% of jailbreaks at a 7.44% false positive rate, and the prefix and suffix heuristic caught 49 of 50 GCG-style attacks at 0.04%. Both heuristics are English only and produce more false positives on code. Injection detection rules for code, SQL, template, and XSS payloads can reject or omit the output.

• Best for: Engineering teams that want detection, topic rails, and output checks in one open source library.
• Covers: Jailbreak heuristics, NIM jailbreak detection, injection rules, input and output rails.
• Pricing (October 2026): Free under Apache-2.0 on GitHub; NIM models need NVIDIA AI Enterprise for production support.
• Watch out for: Heuristics alone catch a minority of jailbreaks, so pair them with a trained classifier.

9. PIGuard: Best Open Model for Fewer False Positives

PIGuard is the open model to try first if false alarms are hurting your agents, because it was trained to ignore trigger words such as ignore and override in benign text. The ACL 2025 paper introduced both the model and the NotInject benchmark that measures this over-defense problem.

In our run, PIGuard flagged 39 of 339 NotInject prompts, compared with 147 for ProtectAI v2 and 242 for deepset. It also had the highest balanced score on our agent corpus. The model needs trust_remote_code=True in Transformers, so review the small custom class file before loading it in production.

• Best for: Teams self-hosting a detector who care most about benign developer and business text.
• Covers: Binary benign or injection labels, DeBERTa-v3-base, English.
• Pricing (October 2026): Free under the MIT license.
• Watch out for: Research project with no vendor support, and it still missed tool description poisoning in our corpus.

How Do These Tools Compare on Agent Coverage?

Lakera is the only tool here that documents tool output screening, inline blocking, and self-hosting together, while the open models give you weights and tuning but leave blocking to your code. We built the matrix below from each vendor’s own docs and model cards on October 25, 2026, so it shows documented support rather than roadmap claims.

Partial ratings mostly mean the detector screens any text you send, but nothing hooks tool results for you. For gateway-based routing, see our LLM gateways guide and MCP gateways guide.

Which Prompt Injection Detection Tool Should You Choose?

Start from where your agents run and where untrusted text enters them, because those two facts remove most of this list. Match your situation to one profile below, then run a two-week pilot with your own benign tool outputs alongside known attacks, and track false positives as closely as catches.

Your situation	First choice	Also consider
Agents read tool output from many MCP servers	Lakera Guard	Prisma AIRS, PIGuard
Building on Azure OpenAI or Foundry	Azure Prompt Shields	Lakera Guard
Running models on Bedrock	Bedrock Guardrails plus InvokeGuardrailChecks on tool output	Lakera Guard
Running Gemini or Vertex AI	Google Model Armor	Lakera Guard
Standardized on Palo Alto Networks	Prisma AIRS	Lakera Guard
Standardized on CrowdStrike Falcon	Falcon AIDR / Guardian	Lakera Guard
Need self-hosted weights with published metrics	Llama Prompt Guard 2	PIGuard
Need an open framework with layered rails	NVIDIA NeMo Guardrails	Prompt Guard 2 inside NeMo

Verdict: Cloud shields win on convenience, Lakera wins on documented agent coverage, and open models win on control and cost. Most production agents should run a detector at tool output and keep risky actions behind approval.

You ship agents with write access

• Screen every tool result and new MCP tool description before the agent plans its next step.
• Pair detection with least-privilege tools from our MCP security tools guide.
• Plan containment using the NIST, NSA, and CISA practices for rogue agents.

You run on one cloud

• Turn on your cloud shield first, then check whether it covers tool results in your agent framework.
• On Bedrock, call InvokeGuardrailChecks on tool output because Converse skips it.
• On Azure, turn on document attack detection, which is off by default in Foundry.

You want to self-host

• Start with PIGuard or Prompt Guard 2 and measure false positives on 200 of your own benign tool outputs.
• Chunk long tool output into 512-token windows and flag the item if any chunk scores high.
• Add the detector to your agent testing and evaluation pipelines so regressions show up before release.

What We Left Off This List

We left off tools that are archived, unmaintained, or built for attack testing rather than runtime detection on October 25, 2026. Several are still useful as research inputs. Our AI agent security tools guide and AI agent governance guide track the wider field.

• LLM Guard: the GitHub repository is archived after a last push on July 8, 2026, though its ProtectAI v2 model still downloads, and we tested it above.
• Rebuff and Vigil: Rebuff is archived and Vigil has not shipped updates since early 2024.
• deepset DeBERTa injection: the model flagged 96% of benign agent outputs in our test, which rules it out for agents without retraining.
• garak, PyRIT, and promptfoo: strong red-team tools for finding injection paths, but they test your stack and do not screen live traffic.
• HiddenLayer, Mindgard, and Pillar Security: broader AI security platforms; we track them in other comparisons.

Frequently Asked Questions

Related Guides

Cloud-native detectors behave differently on agent tool output. Our Bedrock Guardrails vs Azure Prompt Shields vs Model Armor guide sizes 36 real tool outputs against each vendor’s limits and billing units.

For related coverage, see our AI guardrails tools, NeMo Guardrails vs Guardrails AI vs Llama Guard comparison, Lakera alternatives, Lakera vs HiddenLayer vs Mindgard breakdown, AI red teaming tools, Promptfoo vs Garak vs PyRIT, Protect AI alternatives, Prompt Security alternatives, AI-SPM tools guide, AI coding agent security tools and MCP registries roundup.

Keep going with the agent security hub, the AI governance and security hub, our AI agent security tools roundup, the AI agent governance guide, Zenity vs Noma vs Pillar, MCP security tools, MCP gateways, MCP explained, and the ToFu MCP tool security bugs study.

More context sits in FastMCP security advisories, the NIST and CISA incident response practices, Microsoft Dynamic Agent Risk, governance and compliance coverage, editorial standards, disclosure, and contact.

The Bottom Line

Choose Lakera Guard for agent tool outputs on any cloud, your cloud shield for the fastest start, Prisma AIRS or Falcon AIDR if those platforms run your SOC, and Prompt Guard 2, NeMo, or PIGuard to self-host. Put prompt injection detection at every point where untrusted text enters your agent, especially tool results.

Detectors change quickly, so rerun your own benign and attack samples after each model or vendor update and check this page for refreshed results. Send corrections or your own test data through our contact page.