--- title: "vigil-llm vs AI-Infra-Guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deadbits-vigil-llm-vs-tencent-ai-infra-guard" tools: ["deadbits-vigil-llm", "tencent-ai-infra-guard"] --- # vigil-llm vs AI-Infra-Guard *GraphCanon updated Aug 21, 2026* ## Verdict Pick vigil-llm if vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs; pick AI-Infra-Guard if aI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. [vigil-llm](https://vigil.deadbits.ai/) reports 496 GitHub stars, 56 forks, and 16 open issues, last pushed Jan 31, 2024. [AI-Infra-Guard](https://tencent.github.io/AI-Infra-Guard/) has 4.3k stars, 419 forks, and 13 open issues, last pushed Jul 28, 2026. Figures are from public GitHub metadata via [vigil-llm's repository](https://github.com/deadbits/vigil-llm) and [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard). | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Tagline | Detect prompt injections and other risky inputs in LLMs | A full-stack AI Red Teaming platform securing AI ecosystems | | Stars | 496 | 4,316 | | Forks | 56 | 419 | | Open issues | 16 | 13 | | Language | Python | Python | | Adopt for | Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs. | AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 932d | 0d | | Open issues (now) | 16 | 13 | | Stars delta | +5 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/deadbits-vigil-llm/trust.md) | [trust report](/tools/tencent-ai-infra-guard/trust.md) | ## Shared compatibility - **Python**: [vigil-llm](/tools/deadbits-vigil-llm.md) - Python runtime; [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) - Python runtime ## Decision facts: vigil-llm - **Adopt for:** Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs. ## Decision facts: AI-Infra-Guard - **Adopt for:** AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. ## Choose when ### Choose vigil-llm if… - Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection. - When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior. ### Choose AI-Infra-Guard if… - Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools. - Also covers LLM Frameworks. - If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ## When NOT to use vigil-llm - If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary. - For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead. ## When NOT to use AI-Infra-Guard - Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. - Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ## Common questions ### What is the difference between vigil-llm and AI-Infra-Guard? vigil-llm: Detect prompt injections and other risky inputs in LLMs. AI-Infra-Guard: A full-stack AI Red Teaming platform securing AI ecosystems. See the comparison table for live GitHub stats and shared categories. ### When should I choose vigil-llm over AI-Infra-Guard? Choose vigil-llm over AI-Infra-Guard when Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection; When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior. ### When should I choose AI-Infra-Guard over vigil-llm? Choose AI-Infra-Guard over vigil-llm when Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools; Also covers LLM Frameworks; If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ### When should I avoid vigil-llm? If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary. For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead. ### When should I avoid AI-Infra-Guard? Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ### Is vigil-llm or AI-Infra-Guard more popular on GitHub? AI-Infra-Guard has more GitHub stars (4,316 vs 496). Stars measure visibility, not whether either tool fits your constraints. ### Are vigil-llm and AI-Infra-Guard open source? Yes - both are open-source projects on GitHub (vigil-llm: Apache-2.0, AI-Infra-Guard: Apache-2.0). ### Where can I find alternatives to vigil-llm or AI-Infra-Guard? GraphCanon lists graph-backed alternatives at [vigil-llm alternatives](/tools/deadbits-vigil-llm/alternatives) and [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) ([vigil-llm markdown twin](/tools/deadbits-vigil-llm/alternatives.md), [AI-Infra-Guard markdown twin](/tools/tencent-ai-infra-guard/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/deadbits-vigil-llm-vs-tencent-ai-infra-guard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vigil-llm or AI-Infra-Guard? vigil-llm: Dormant. AI-Infra-Guard: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for vigil-llm and AI-Infra-Guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vigil-llm trust report](/tools/deadbits-vigil-llm/trust); [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deadbits-vigil-llm`](/api/graphcanon/graph?tool=deadbits-vigil-llm) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_