Comparison
PromptAttack vs circle-guard-bench
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.
Markdown twin · PromptAttack alternatives · circle-guard-bench alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | PromptAttack | circle-guard-bench |
|---|---|---|
| Maintenance | Dormant (560d since push) As of 2w · github_public_v1 | Slowing (154d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- PromptAttack
- An LLM can Fool Itself: A Prompt-Based Adversarial Attack
- circle-guard-bench
- AI benchmark for evaluating LLM guard systems
Stars
- PromptAttack
- 117
- circle-guard-bench
- 72
Forks
- PromptAttack
- 17
- circle-guard-bench
- 5
Open issues
- PromptAttack
- 0
- circle-guard-bench
- 0
Language
- PromptAttack
- Python
- circle-guard-bench
- Python
Adopt for
- PromptAttack
- 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
- circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios.
Persona
- PromptAttack
- -
- circle-guard-bench
- -
Runtime
- PromptAttack
- -
- circle-guard-bench
- -
License
- PromptAttack
- -
- circle-guard-bench
- Apache-2.0
Last pushed
- PromptAttack
- Jan 21, 2025
- circle-guard-bench
- Mar 7, 2026
Categories
- PromptAttack
- Evaluation & Observability
- circle-guard-bench
- Evaluation & Observability
Trust and health
Maintenance
- PromptAttack
- Dormant (18%)
- circle-guard-bench
- Slowing (36%)
Days since push
- PromptAttack
- 560d
- circle-guard-bench
- 154d
Owner type
- PromptAttack
- User
- circle-guard-bench
- Organization
OSV dependency advisories
- PromptAttack
- Published findings
- circle-guard-bench
- No lockfile (source not queried)
Full report
- PromptAttack
- Trust report
- circle-guard-bench
- Trust report
Shared compatibility
- Python · PromptAttack: Python runtime · circle-guard-bench: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (GodXuxilie/PromptAttack) · observed Aug 5, 2026
- GitHub forks (GodXuxilie/PromptAttack) · observed Aug 5, 2026
- Last push (GodXuxilie/PromptAttack) · observed Jan 21, 2025
- License file (unknown) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (whitecircle/circle-guard-bench) · observed Aug 8, 2026
- GitHub forks (whitecircle/circle-guard-bench) · observed Aug 8, 2026
- Last push (whitecircle/circle-guard-bench) · observed Mar 7, 2026
- License file (Apache-2.0) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: PromptAttack 117 · circle-guard-bench 72 (synced Aug 5, 2026).
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 and circle-guard-bench alternatives (PromptAttack markdown twin, circle-guard-bench markdown twin), 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 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; circle-guard-bench trust report.