If you are evaluating the best prompt injection detection tools for a production LLM application in 2026, the field has consolidated to four serious options: Lakera Guard (now part of Check Point) if you want a dedicated vendor with the strongest published detection numbers, Azure Prompt Shields or Amazon Bedrock Guardrails if you are already committed to that cloud, and Meta’s Llama Prompt Guard 2 if you need open weights you can run inside your own perimeter. Our working recommendation: cloud-native teams should start with their platform’s built-in filter and only add a dedicated product when its false-negative rate on their own traffic justifies the spend. Teams with data-residency constraints should start from Prompt Guard 2 and accept that they own the tuning.
Prompt injection is LLM01 in the OWASP Top 10 for LLM Applications, and no detector eliminates it. Every tool below is a classifier sitting in the request path, and classifiers get evaded. Microsoft’s own Prompt Shields documentation says plainly that the service “may not catch all attack vectors” and to “always implement additional validation layers.” Treat everything here as one layer, not a fix.
What the leading tools do
Lakera Guard is the dedicated-vendor option. Per Lakera’s site, it detects prompt injection, jailbreaks, sensitive-data exposure, and unsafe agent tool use, integrates as a REST API with claimed sub-50 ms runtime latency, observes 100+ languages, and ships in both SaaS and self-hosted form. Lakera was acquired by Check Point, and the product now sits in that portfolio, which matters for procurement: you are buying a Check Point relationship, not a startup’s.
Azure Prompt Shields, part of Azure AI Content Safety, is a unified API that classifies two distinct things: user prompt attacks (rule-changing, persona replacement, embedded conversation mockups, encoding attacks) and document attacks, meaning indirect injections hidden in third-party content such as retrieved documents or email. That second category is the one RAG teams should care most about; the difference is covered in depth in direct vs. indirect prompt injection. Per Microsoft’s docs, the models are trained and tested on eight languages (English, Chinese, French, German, Spanish, Italian, Japanese, Portuguese), with degraded quality outside them.
Amazon Bedrock Guardrails folds prompt-attack detection into a broader policy engine: content filters that cover “prompt injections and jailbreaks,” plus denied topics, PII redaction, and contextual grounding checks. The notable capability, per AWS, is the ApplyGuardrail API, which evaluates content without invoking a foundation model, so the same guardrail can front models hosted outside Bedrock, including OpenAI and Gemini endpoints.
Llama Prompt Guard 2 is Meta’s open-weights classifier, released in 86M and 22M parameter sizes. Per the model card, it is a binary benign/malicious classifier built on mDeBERTa-base (86M) and DeBERTa-xsmall (22M), scoped deliberately to prompts that attempt to override prior instructions rather than to harmful content generally. The 86M model reports an AUC of 0.998 on English attacks and 97.5% recall at a 1% false-positive rate; the 22M variant trades that down to 88.7% recall at 1% FPR for roughly a quarter of the compute. Licensing is the Llama 4 Community License, not MIT, which some legal teams will want to review.
One tool you will still see in older roundups deserves a warning label: LLM Guard, Protect AI’s open-source scanner toolkit, was a reasonable self-hosted default for two years, but the repository was archived in July 2026 and its Hugging Face models are no longer maintained. Do not put an unmaintained classifier in a security path; attackers iterate faster than a frozen model.
| Capability | Lakera Guard | Azure Prompt Shields | Bedrock Guardrails | Llama Prompt Guard 2 |
|---|---|---|---|---|
| Direct injection detection | Yes | Yes | Yes | Yes |
| Indirect / document attack scanning | Yes, incl. agent tool responses | Yes (document shield) | Via content filters on any input | Model-level only |
| Jailbreak detection | Yes, separate category | Yes | Yes, same filter class | Scoped to instruction-override |
| Additional scanners | PII and content policies | Wider Content Safety suite | Denied topics, PII, grounding checks | None; bare classifier |
| Deployment | SaaS API or self-hosted | Azure-hosted API | AWS API, portable via ApplyGuardrail | Self-hosted open weights |
| Language coverage | 100+ (vendor claim) | 8 trained languages | Not published per-language | 8 listed for the 86M model |
What the benchmark evidence says
The one independent academic comparison is the Palit benchmark study (Palit and Woods, May 2025), which surveyed 13 LLM security tools and formally evaluated seven. On its no-context test set, Lakera Guard led on precision at 0.964 with 0.501 recall, a 0.057 false-positive rate, and 0.066 seconds per prompt. Protect AI’s LLM Guard posted the highest recall in the group at 0.604 with 0.938 precision, but at 1.59 seconds per prompt. Azure Prompt Shield came in at 0.945 precision and 0.364 recall with 0.349 seconds of added latency, and the open-source Vigil scanner reached comparable precision (0.944) at 2.94 seconds per prompt, which rules it out of any synchronous request path.
Read the recall column again before signing anything. The best-performing tool in an independent evaluation caught roughly half the attacks in the set. The authors’ conclusion was that Lakera Guard and LLM Guard were the strongest of the group, not that any of them closed the gap — which is the same position OWASP takes when it lists filtering as one of several mitigations rather than a fix.
The most-cited public comparison is Lakera’s PINT benchmark, 4,314 inputs mixing injections, jailbreaks, benign look-alikes, and ordinary documents across multiple language families. Its published scores: Lakera Guard 95.22%, Bedrock Guardrails 89.24%, Azure Prompt Shield 89.12%, Llama Prompt Guard 2 78.76%, Google Model Armor 70.07%.
Two caveats before you weight those numbers. First, PINT is published by Lakera, and the vendor scoring first on its own benchmark is exactly the pattern an evaluation memo should discount; the useful signal is the relative clustering of the non-Lakera tools, not the winner. Second, the repository was itself archived in August 2026, so the scores are a snapshot, not a living leaderboard. Prompt Guard 2’s lower PINT score against its excellent model-card numbers also illustrates a real phenomenon: a classifier scoped narrowly to instruction-override attacks will score lower on a suite that includes broader manipulation attempts. Neither number is wrong; they measure different definitions of “attack.”
Where the detector sits, and what it costs you
All four tools occupy the same architectural slot: a runtime filter between your application and the model. The standard integration is a synchronous pre-flight check — the request arrives, your orchestration layer sends the prompt and, if you run RAG, the retrieved context to the detector, and only a clean verdict lets the call proceed. That placement is why the latency figures below are load-bearing: the detector’s inference time lands directly on your time-to-first-token.
Inbound scanning of the user’s message is the easy half. Scanning retrieved context is where document-level classification like Prompt Shields’ earns its keep, because retrieval poisoning bypasses any filter that only reads what the user typed. Teams running agents need a third checkpoint that most designs omit: tool and API responses, screened before they re-enter the context window, since that is where indirect injection actually lands. Outbound scanning of the model’s own response is a separate control the cloud suites bundle and the pure detectors do not; how those layers stack is laid out well in this overview of LLM guardrails.
The trade-offs to price in:
- Latency. Every scan is a serial hop. Lakera claims sub-50 ms; Meta’s model card reports 92.4 ms per classification for the 86M model and 19.3 ms for the 22M on an A100 at 512 tokens. Self-hosting means you also own the GPU capacity planning.
- False positives. Meta publishes recall at a fixed 1% FPR, which is the honest way to report it. The cloud vendors mostly do not publish FPR, so budget a pilot on your real traffic before enforcement mode; a filter that blocks 1 in 100 legitimate prompts will get switched off by your own product team.
- Language coverage. Prompt Shields lists eight trained languages, Prompt Guard 2’s 86M model lists eight (a different eight, including Hindi and Thai), Lakera claims 100+. If your users write Turkish or Vietnamese, test before trusting.
- Lock-in. Bedrock’s ApplyGuardrail is the most portable of the cloud options; Prompt Shields assumes an Azure resource and region availability.
Who should pick what
Pick Lakera Guard if you are multi-cloud or agent-heavy, want jailbreak detection, PII screening, and tool-misuse checks from one API, and have a budget for a dedicated product. Pick Prompt Shields if you are an Azure shop running RAG; its explicit document-attack classification is the differentiator. Pick Bedrock Guardrails if you are on AWS or need one policy engine across mixed model providers. Pick Llama Prompt Guard 2 if prompts cannot leave your infrastructure, you have MLOps capacity to monitor and retrain a classifier, and your legal team clears the Llama license.
Skip a dedicated detector entirely, for now, if you run a low-stakes internal chatbot with no tool access and no retrieval: the platform-level defaults plus output validation cover most of that risk profile. And whichever you choose, keep it inside a layered design with least-privilege tool access and output handling, because detection is the layer that fails noisily, not the one that fails safe.
Whichever candidates make the shortlist, the deciding number is the one measured on your own traffic rather than on a vendor’s. Building a held-out set with realistic hard negatives is the work that decides a rollout, because the false-positive rate on benign-but-security-adjacent text is what gets a filter switched off, not the detection rate on obvious attacks — the method is walked through in how to benchmark LLM guardrails.
Related across the network
- Jailbreak Detection for LLMs Explained: How Runtime Filters Work — aidefense.dev
- How to Benchmark LLM Guardrails on Your Own Traffic — aidefense.dev
- How to Detect Jailbreak Prompts: A Practitioner’s Guide — aimoderationtools.com
- How to Mitigate Prompt Injection Attacks: Defense in Depth — aimoderationtools.com
- Prompt Injection Detection Tools: A Practitioner’s Evaluation — aimoderationtools.com
- Prompt Injection Attack Compendium (2026 Edition) — aisec.blog
- Rebuff: Open-Source Prompt Injection Defense, Layer by Layer — aisecreviews.com
