Comparison
PromptAttack vs Awesome-LLM-hallucination
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 Awesome-LLM-hallucination if awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it distinct from other tools,.
Markdown twin · PromptAttack alternatives · Awesome-LLM-hallucination alternatives
GraphCanon updated Oct 5, 2026
14views this month
vs
Trust & integrity
| Signal | PromptAttack | Awesome-LLM-hallucination |
|---|---|---|
| Maintenance | Dormant (622d since push) As of Oct 5, 2026 · github_public_v1 | Dormant (908d since push) As of Sep 6, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 5, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 6, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- Awesome-LLM-hallucination
- A Survey on Hallucination in Large Language Models
Stars
- PromptAttack
- 117
- Awesome-LLM-hallucination
- 338
Forks
- PromptAttack
- 17
- Awesome-LLM-hallucination
- 25
Open issues
- PromptAttack
- 0
- Awesome-LLM-hallucination
- 4
Language
- PromptAttack
- Python
- Awesome-LLM-hallucination
- -
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.
- Awesome-LLM-hallucination
- Awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it distinct from other tools,
Persona
- PromptAttack
- -
- Awesome-LLM-hallucination
- -
Runtime
- PromptAttack
- -
- Awesome-LLM-hallucination
- -
License
- PromptAttack
- -
- Awesome-LLM-hallucination
- MIT
Last pushed
- PromptAttack
- Jan 21, 2025
- Awesome-LLM-hallucination
- Mar 11, 2024
Categories
- PromptAttack
- Evaluation & Observability
- Awesome-LLM-hallucination
- Evaluation & Observability
Trust and health
Days since push
- PromptAttack
- 622d
- Awesome-LLM-hallucination
- 908d
Open issues (now)
- PromptAttack
- 0
- Awesome-LLM-hallucination
- 4
Stars delta
- PromptAttack
- 0 (30d)
- Awesome-LLM-hallucination
- -1 (30d)
OSV dependency advisories
- PromptAttack
- Published findings
- Awesome-LLM-hallucination
- No lockfile (source not queried)
Full report
- PromptAttack
- Trust report
- Awesome-LLM-hallucination
- Trust report
Shared compatibility
- ChatGPT · PromptAttack: Works with ChatGPT · Awesome-LLM-hallucination: Works with ChatGPT
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 recently updated (last pushed Jan 21, 2025).
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 Awesome-LLM-hallucination if…
- Requirements: The exact language used by the repository is unknown, as no specific programming languages are listed..
- Tags unique to Awesome-LLM-hallucination: hallucination, large-language-models, llm, survey.
- - When you need detailed categorizations by causes, detection methods, and mitigation strategies for LLM hallucinations.
When NOT to use Awesome-LLM-hallucination
- - Avoid using this resource for practical, hands-on tools or code that helps mitigate hallucinations directly (it's primarily informative).
- - Do not use if you are looking for real-time diagnostic software for identifying and correcting LLM hallucination mistakes in live applications.
- - This tool is not suitable as a standalone guide for implementing mitigation techniques within your own large language models; it lacks detailed technical instructions.
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 Oct 5, 2026
- GitHub forks (GodXuxilie/PromptAttack) · observed Oct 5, 2026
- Last push (GodXuxilie/PromptAttack) · observed Jan 21, 2025
- License file (unknown) · observed Oct 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (LuckyyySTA/Awesome-LLM-hallucination) · observed Sep 20, 2026
- GitHub forks (LuckyyySTA/Awesome-LLM-hallucination) · observed Sep 20, 2026
- Last push (LuckyyySTA/Awesome-LLM-hallucination) · observed Mar 11, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: PromptAttack 117 · Awesome-LLM-hallucination 338 (synced Oct 5, 2026).
Common questions
- What is the difference between PromptAttack and Awesome-LLM-hallucination?
- PromptAttack: An LLM can Fool Itself: A Prompt-Based Adversarial Attack. Awesome-LLM-hallucination: A Survey on Hallucination in Large Language Models. See the comparison table for live GitHub stats and shared categories.
- When should I choose PromptAttack over Awesome-LLM-hallucination?
- Choose PromptAttack over Awesome-LLM-hallucination when Tags unique to PromptAttack: adversarial attack, language model evaluation, prompt-engineering; For targeted analysis of adversarial robustness in specific language models; More recently updated (last pushed Jan 21, 2025).
- When should I choose Awesome-LLM-hallucination over PromptAttack?
- Choose Awesome-LLM-hallucination over PromptAttack when Requirements: The exact language used by the repository is unknown, as no specific programming languages are listed.; Tags unique to Awesome-LLM-hallucination: hallucination, large-language-models, llm, survey; - When you need detailed categorizations by causes, detection methods, and mitigation strategies for LLM hallucinations.
- 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 Awesome-LLM-hallucination?
- - Avoid using this resource for practical, hands-on tools or code that helps mitigate hallucinations directly (it's primarily informative). - Do not use if you are looking for real-time diagnostic software for identifying and correcting LLM hallucination mistakes in live applications. - This tool is not suitable as a standalone guide for implementing mitigation techniques within your own large language models; it lacks detailed technical instructions.
- Is PromptAttack or Awesome-LLM-hallucination more popular on GitHub?
- Awesome-LLM-hallucination has more GitHub stars (338 vs 117). Stars measure visibility, not whether either tool fits your constraints.
- Are PromptAttack and Awesome-LLM-hallucination open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to PromptAttack or Awesome-LLM-hallucination?
- GraphCanon lists graph-backed alternatives at PromptAttack alternatives and Awesome-LLM-hallucination alternatives (PromptAttack markdown twin, Awesome-LLM-hallucination 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 Awesome-LLM-hallucination?
- PromptAttack: Dormant. Awesome-LLM-hallucination: Dormant. 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 Awesome-LLM-hallucination?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: PromptAttack trust report; Awesome-LLM-hallucination trust report.