All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
This analysis is scoped to LlamaFirewall.
LlamaFirewall is a free, self-hosted guardrail from Meta that checks AI agents for jailbreaks, goal misalignment, and insecure generated code, with the detector models posted on Hugging Face under the Llama community license. Meta charges nothing and documents no hosted service, so its payoff is safer agents built on Llama rather than guardrail revenue. The public record shows capable research with no commercial motion attached. For paid guardrail vendors the likely effect is price pressure, because buyers can now weigh their prompt-injection checks against a free option this capable from the owner of the Llama platform.
| Description | LlamaFirewall is an open-source guardrail framework from Meta that detects security risks in LLM chat and multi-step AI agents, with scanners for prompt injection, agent misalignment, and insecure generated code. | [f1] |
|---|---|---|
| Deployment | Self-hosted | [f2] |
| Product | What it does |
|---|---|
| LlamaFirewall | LlamaFirewall: Open-source guardrail framework from Meta that scans LLM apps and agents with PromptGuard 2, AlignmentCheck, and CodeShield scanners. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
LlamaFirewall is an open-source guardrail framework from Meta that scans LLM apps and agents with the PromptGuard 2, AlignmentCheck, and CodeShield scanners. It is mapped to the AI Defense Matrix. [f3]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score |
|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs, demos, and third-party validation. | 4/5 |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 4/5 |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 2/5 |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 2/5 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
| Dimension | Score |
|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 1/3 |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Meta: LlamaFirewall on GitHub (PurpleLlama) | official | 2026-07-09 |
| f2 | AI Defense Matrix Catalog entry | other | 2026-06-10 |
| f3 | AI Defense Matrix Catalog mapping | other | 2026-06-23 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | PurpleLlama repository on GitHub “Set of tools to assess and improve LLM security.” | official | 2026-06-14 |
| s2 | LlamaFirewall README in the PurpleLlama repository “LlamaFirewall is a framework designed to detect and mitigate AI centric security risks, supporting multiple layers of inputs and outputs, such as typical LLM chat and more advanced multi-step agentic operations.” | official | 2026-06-14 |
| s3 | LlamaFirewall: An open source guardrail system for building secure AI agents (arXiv) “We introduce LlamaFirewall, an open-source security focused guardrail framework designed to serve as a final layer of defense against security risks associated with AI Agents.” | research | 2026-06-18 |
| s4 | LlamaFirewall documentation site “Optimized for minimal computational overhead, ensuring negligible impact on performance and user experience.” | official | 2026-06-14 |
| s5 | AI Defense Matrix Catalog: LlamaFirewall “Open-source guardrail framework from Meta that scans LLM apps and agents with PromptGuard 2, AlignmentCheck, and CodeShield scanners.” | other | 2026-06-14 |
| s6 | llamafirewall package on PyPI “LlamaFirewall is a framework designed to detect and mitigate AI centric security risks” | official | 2026-06-18 |
| s7 | Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models (arXiv) “This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants.” | research | 2026-06-30 |
| s8 | InfoQ: Meta Open Sources LlamaFirewall for AI Agent Combined Protection “LlamaFirewall is a security framework aimed at safeguarding AI agents against prompt injection, goal misalignment, and insecure code generation. It achieved over 90% efficacy in reducing attack success rates when evaluated on the AgentDojo benchmark.” | press | 2026-06-30 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | PurpleLlama repository on GitHub “evals and benchmarks are licensed under the MIT license while any models use the corresponding Llama Community license ... Safeguard Llama Guard ... Safeguard Prompt Guard ... Llama 3.2 Community License” | official | 2026-06-18 |
| s2 | LlamaFirewall README in the PurpleLlama repository “Preload the Model to your local cache directory, ~/.cache/huggingface ... for any missing model, LlamaFirewall will automate the download” | official | 2026-06-18 |
| s3 | LlamaFirewall: An open source guardrail system for building secure AI agents (arXiv) “PromptGuard 2, a universal jailbreak detector ... Agent Alignment Checks, a chain-of-thought auditor that inspects agent reasoning for prompt injection and goal misalignment, which, while still experimental ... and CodeShield, an online static analysis engine.” | research | 2026-06-14 |
| s4 | LlamaFirewall documentation site “Optimized for minimal computational overhead, ensuring negligible impact on performance and user experience.” | official | 2026-06-18 |
| s5 | AI Defense Matrix Catalog: LlamaFirewall “Open-source guardrail framework from Meta that scans LLM apps and agents with PromptGuard 2, AlignmentCheck, and CodeShield scanners.” | other | 2026-06-14 |
| s6 | llamafirewall package on PyPI “LlamaFirewall is a framework designed to detect and mitigate AI centric security risks” | official | 2026-06-14 |
| s7 | Meta: Llama Prompt Guard 2 86M model card on Hugging Face “Safetensors ... Model size 0.3B params ... Downloads last month 103,394 ... We use mDeBERTa-base for the base version of Llama Prompt Guard 2 86M, and DeBERTa-xsmall as the base model for Llama Prompt Guard 2 22M. Both are open-source, MIT-licensed models from Microsoft.” | official | 2026-06-18 |
| s8 | Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (arXiv) “We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.” | research | 2026-06-30 |
| s9 | InfoQ: Meta Open Sources LlamaFirewall for AI Agent Combined Protection “LlamaFirewall is a security framework aimed at safeguarding AI agents against prompt injection, goal misalignment, and insecure code generation. It achieved over 90% efficacy in reducing attack success rates when evaluated on the AgentDojo benchmark.” | press | 2026-06-30 |
This site is an experimental research aid created by Zeltser Security Corp. All its data gathering and analysis was performed autonomously without human review, and it can contain errors of fact, interpretation, and judgment that a human reviewer might catch.
The analyses are statements of opinion, not statements of fact. Machine analysis produced the scores, summaries, and matrix placements by weighing the public sources each page cites, and reasonable people can weigh the same sources differently. Where a page states a fact, it cites the public source and the date it was checked, and the statement is only as accurate as that source. Unless a profile expressly says otherwise, the analysis involves no hands-on testing and no independent validation of any company's products or services.
Nothing here is professional, security, legal, financial, investment, or purchasing advice, and nothing here is a recommendation to invest in, do business with, or avoid any company. Inclusion of a company is not an endorsement, and absence of a company is not a judgment about it. Reading this site creates no advisory or client relationship. Verify any detail you plan to act on against the vendor's current materials.
The content is provided "as is" and "as available," with all warranties disclaimed, express or implied, including merchantability, fitness for a particular purpose, accuracy, and non-infringement. No entry is warranted to be complete, current, or correct. Companies change, vendors update their claims, sources can be wrong, and automated analysis can misread them.
To the fullest extent permitted by law, the operator, Zeltser Security Corp, is not liable for any damages that arise from using this site or relying on its content, including direct, indirect, incidental, special, and consequential damages and lost profits, even if advised that such damages were possible. If you are dissatisfied with the site or disagree with these terms, your remedy is to stop using it.
Entries link to vendor pages, press coverage, and other external sites that Zeltser Security Corp does not control and is not responsible for. A link is not an affiliation with the destination or an endorsement of it. Product and company names and trademarks are the property of their owners, used here nominatively to identify the companies described. Short quotations from cited sources appear for identification and commentary.
Do not republish its content or share access without the operator's permission.