Comparison
awesome-hallucination-detection vs rebuff
Verdict
Pick awesome-hallucination-detection if awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA; pick rebuff if rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license.
Markdown twin · awesome-hallucination-detection alternatives · rebuff alternatives
GraphCanon updated 2w
awesome-hallucination-detection
EdinburghNLP/awesome-hallucination-detection
★ 1.1kpushed Jul 24, 2026
Trust & integrity
| Signal | awesome-hallucination-detection | rebuff |
|---|---|---|
| Maintenance | Active (12d since push) As of 2w · github_public_v1 | Archived (727d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- awesome-hallucination-detection
- List of papers on hallucination detection in LLMs.
- rebuff
- LLM Prompt Injection Detector
Stars
- awesome-hallucination-detection
- 1.1k
- rebuff
- 1.5k
Forks
- awesome-hallucination-detection
- 91
- rebuff
- 141
Open issues
- awesome-hallucination-detection
- 0
- rebuff
- 33
Language
- awesome-hallucination-detection
- -
- rebuff
- TypeScript
Adopt for
- awesome-hallucination-detection
- awesome-hallucination-detection provides a curated list of research papers focused on techniques to detect and mitigate hallucinations in large language models (LLMs), including process supervision methods for factual QA
- rebuff
- Rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license.
Persona
- awesome-hallucination-detection
- -
- rebuff
- -
Runtime
- awesome-hallucination-detection
- -
- rebuff
- -
License
- awesome-hallucination-detection
- Apache-2.0
- rebuff
- Apache-2.0
Last pushed
- awesome-hallucination-detection
- Jul 24, 2026
- rebuff
- Aug 7, 2024
Categories
- awesome-hallucination-detection
- Evaluation & Observability
- rebuff
- Evaluation & Observability
Trust and health
Maintenance
- awesome-hallucination-detection
- Active (82%)
- rebuff
- Archived (8%)
Days since push
- awesome-hallucination-detection
- 12d
- rebuff
- 727d
Archived on GitHub
- awesome-hallucination-detection
- No
- rebuff
- Yes
Open issues (now)
- awesome-hallucination-detection
- 0
- rebuff
- 33
Full report
- awesome-hallucination-detection
- Trust report
- rebuff
- Trust report
Choose awesome-hallucination-detection if…
- Tags unique to awesome-hallucination-detection: evaluation, hallucination, llms, nlp.
- - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat
- More recently updated (last pushed Jul 24, 2026).
When NOT to use awesome-hallucination-detection
- When immediate implementation or code is needed rather than research papers, this repository is not suitable as it only curates information on methodologies and benchmarks.
- - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration
Choose rebuff if…
- Tags unique to rebuff: llm, llmops, prompt-engineering, prompt-injection.
- Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models.
- More GitHub stars (1.5k vs 1.1k) - visibility, not fit.
When NOT to use rebuff
- Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (EdinburghNLP/awesome-hallucination-detection) · observed Aug 6, 2026
- GitHub forks (EdinburghNLP/awesome-hallucination-detection) · observed Aug 6, 2026
- Last push (EdinburghNLP/awesome-hallucination-detection) · observed Jul 24, 2026
- License file (Apache-2.0) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (protectai/rebuff) · observed Aug 5, 2026
- GitHub forks (protectai/rebuff) · observed Aug 5, 2026
- Last push (protectai/rebuff) · observed Aug 7, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-hallucination-detection 1.1k · rebuff 1.5k (synced Aug 6, 2026).
Common questions
- What is the difference between awesome-hallucination-detection and rebuff?
- awesome-hallucination-detection: List of papers on hallucination detection in LLMs.. rebuff: LLM Prompt Injection Detector. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-hallucination-detection over rebuff?
- Choose awesome-hallucination-detection over rebuff when Tags unique to awesome-hallucination-detection: evaluation, hallucination, llms, nlp; - When focusing on specific methodologies like Corpus Verify (CorVer) from the paper 'Verifiable Rewards Beyond Math and Code' which utilizes lightweight, process-based rewards to mitigate hallucinat; More recently updated (last pushed Jul 24, 2026).
- When should I choose rebuff over awesome-hallucination-detection?
- Choose rebuff over awesome-hallucination-detection when Tags unique to rebuff: llm, llmops, prompt-engineering, prompt-injection; Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models; More GitHub stars (1.5k vs 1.1k) - visibility, not fit.
- When should I avoid awesome-hallucination-detection?
- When immediate implementation or code is needed rather than research papers, this repository is not suitable as it only curates information on methodologies and benchmarks. - If your focus is on general LLM training techniques without a specific emphasis on hallucination detection or calibration
- When should I avoid rebuff?
- Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.
- Is awesome-hallucination-detection or rebuff more popular on GitHub?
- rebuff has more GitHub stars (1,516 vs 1,121). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-hallucination-detection and rebuff open source?
- Yes - both are open-source projects on GitHub (awesome-hallucination-detection: Apache-2.0, rebuff: Apache-2.0).
- Where can I find alternatives to awesome-hallucination-detection or rebuff?
- GraphCanon lists graph-backed alternatives at awesome-hallucination-detection alternatives and rebuff alternatives (awesome-hallucination-detection markdown twin, rebuff 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, awesome-hallucination-detection or rebuff?
- awesome-hallucination-detection: Active. rebuff: Archived. 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 awesome-hallucination-detection and rebuff?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-hallucination-detection trust report; rebuff trust report.