--- title: "PromptAttack vs circle-guard-bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/godxuxilie-promptattack-vs-whitecircle-circle-guard-bench" tools: ["godxuxilie-promptattack", "whitecircle-circle-guard-bench"] --- # PromptAttack vs circle-guard-bench *GraphCanon updated Aug 8, 2026* ## Verdict Pick PromptAttack if promptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs; pick circle-guard-bench if circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios. [PromptAttack](https://github.com/GodXuxilie/PromptAttack) reports 117 GitHub stars, 17 forks, and 0 open issues, last pushed Jan 21, 2025. [circle-guard-bench](https://whitecircle.ai) has 72 stars, 5 forks, and 0 open issues, last pushed Mar 7, 2026. Figures are from public GitHub metadata via [PromptAttack's repository](https://github.com/GodXuxilie/PromptAttack) and [circle-guard-bench's repository](https://github.com/whitecircle/circle-guard-bench). | | [PromptAttack](/tools/godxuxilie-promptattack.md) | [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) | | --- | --- | --- | | Tagline | An LLM can Fool Itself: A Prompt-Based Adversarial Attack | AI benchmark for evaluating LLM guard systems | | Stars | 117 | 72 | | Forks | 17 | 5 | | Open issues | 0 | 0 | | Language | Python | Python | | Adopt for | PromptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs. | circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [PromptAttack](/tools/godxuxilie-promptattack.md) | [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 560d | 154d | | Owner type | User | Organization | | Full report | [trust report](/tools/godxuxilie-promptattack/trust.md) | [trust report](/tools/whitecircle-circle-guard-bench/trust.md) | ## Shared compatibility - **Python**: [PromptAttack](/tools/godxuxilie-promptattack.md) - Python runtime; [circle-guard-bench](/tools/whitecircle-circle-guard-bench.md) - Python runtime ## Decision facts: PromptAttack - **Adopt for:** PromptAttack is an LLM-targeted adversarial attack tool that leverages prompt engineering to generate adversarial samples keeping semantic intact but misclassifying outputs. ## Decision facts: circle-guard-bench - **Adopt for:** circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios. ## Choose when ### Choose PromptAttack if… - Tags unique to PromptAttack: adversarial attack, language model evaluation, prompt-engineering. - For targeted analysis of adversarial robustness in specific language models. - More GitHub stars (117 vs 72) - visibility, not fit. ### Choose circle-guard-bench if… - Tags unique to circle-guard-bench: ai, benchmarking, guardrail, large language models. - Use circle-guard-bench when you need to evaluate the effectiveness of guardrails and safeguards in your LLM environment, as it offers an unparalleled set of scenarios specific to these protections. - More recently updated (last pushed Mar 7, 2026). ## When NOT to use PromptAttack - If the focus is on general model improvement rather than adversarial testing. - When working with proprietary or sensitive data that cannot be manipulated via external prompt tools, given potential data leakage concerns. ## When NOT to use circle-guard-bench - Avoid circle-guard-bench if your primary focus is on benchmarking the performance aspects like speed and latency of LLMs, as it specializes in evaluating protections rather than performance. - Do not use this tool when you intend to conduct general purpose evaluations or comparisons between different LLM models that do not specifically involve security-related guard systems. ## Common questions ### What is the difference between PromptAttack and circle-guard-bench? PromptAttack: An LLM can Fool Itself: A Prompt-Based Adversarial Attack. circle-guard-bench: AI benchmark for evaluating LLM guard systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose PromptAttack over circle-guard-bench? Choose PromptAttack over circle-guard-bench when Tags unique to PromptAttack: adversarial attack, language model evaluation, prompt-engineering; For targeted analysis of adversarial robustness in specific language models; More GitHub stars (117 vs 72) - visibility, not fit. ### When should I choose circle-guard-bench over PromptAttack? Choose circle-guard-bench over PromptAttack when Tags unique to circle-guard-bench: ai, benchmarking, guardrail, large language models; Use circle-guard-bench when you need to evaluate the effectiveness of guardrails and safeguards in your LLM environment, as it offers an unparalleled set of scenarios specific to these protections; More recently updated (last pushed Mar 7, 2026). ### When should I avoid PromptAttack? If the focus is on general model improvement rather than adversarial testing. When working with proprietary or sensitive data that cannot be manipulated via external prompt tools, given potential data leakage concerns. ### When should I avoid circle-guard-bench? Avoid circle-guard-bench if your primary focus is on benchmarking the performance aspects like speed and latency of LLMs, as it specializes in evaluating protections rather than performance. Do not use this tool when you intend to conduct general purpose evaluations or comparisons between different LLM models that do not specifically involve security-related guard systems. ### Is PromptAttack or circle-guard-bench more popular on GitHub? PromptAttack has more GitHub stars (117 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are PromptAttack and circle-guard-bench open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to PromptAttack or circle-guard-bench? GraphCanon lists graph-backed alternatives at [PromptAttack alternatives](/tools/godxuxilie-promptattack/alternatives) and [circle-guard-bench alternatives](/tools/whitecircle-circle-guard-bench/alternatives) ([PromptAttack markdown twin](/tools/godxuxilie-promptattack/alternatives.md), [circle-guard-bench markdown twin](/tools/whitecircle-circle-guard-bench/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/godxuxilie-promptattack-vs-whitecircle-circle-guard-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, PromptAttack or circle-guard-bench? PromptAttack: Dormant. circle-guard-bench: Slowing. 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 PromptAttack and circle-guard-bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [PromptAttack trust report](/tools/godxuxilie-promptattack/trust); [circle-guard-bench trust report](/tools/whitecircle-circle-guard-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=godxuxilie-promptattack`](/api/graphcanon/graph?tool=godxuxilie-promptattack) - 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/_