RRepoGEO

REPOGEO REPORT · LITE

protectai/rebuff

Default branch main · commit 4d2fe064 · scanned 5/25/2026, 3:07:02 AM

GitHub: 1,489 stars · 135 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface protectai/rebuff, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's core value proposition

    Why:

    CURRENT
    Rebuff is designed to protect AI applications from prompt injection (PI) attacks through a [multi-layered defense](#features).
    COPY-PASTE FIX
    Rebuff.ai is a self-hardening, multi-layered prompt injection detector designed to protect AI applications from adversarial attacks. It uniquely combines heuristics, LLM-based detection, and attack signature learning via a VectorDB with innovative canary tokens to prevent and learn from prompt injection attempts.
  • mediumtopics#2
    Enhance topics with more specific security keywords

    Why:

    CURRENT
    llm, llmops, prompt-engineering, prompt-injection, prompts, security
    COPY-PASTE FIX
    llm, llmops, prompt-engineering, prompt-injection, prompts, security, llm-security, ai-security, adversarial-ai, prompt-guardrails
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with Alternatives' or 'Why Rebuff?' that briefly outlines how Rebuff's multi-layered defense and canary token system differentiate it from other prompt injection detection methods or general LLM guardrails.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface protectai/rebuff
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
guardrails-ai/guardrails
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. guardrails-ai/guardrails · recommended 2×
  2. Microsoft Azure AI Content Safety · recommended 2×
  3. OpenAI Moderation API · recommended 2×
  4. NVIDIA/NeMo-Guardrails · recommended 2×
  5. langchain-ai/langchain · recommended 2×
  • CATEGORY QUERY
    How to secure my AI application from malicious prompt injection attempts?
    you: not recommended
    AI recommended (in order):
    1. Guardrails AI (guardrails-ai/guardrails)
    2. Microsoft Azure AI Content Safety
    3. OpenAI Moderation API
    4. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    5. LangChain (langchain-ai/langchain)
    6. OWASP Top 10 for LLM Applications
    7. Cloudflare Bot Management / WAF

    AI recommended 7 alternatives but never named protectai/rebuff. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some robust solutions for protecting LLM-powered applications against adversarial prompts?
    you: not recommended
    AI recommended (in order):
    1. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    2. Microsoft Azure AI Content Safety
    3. OpenAI Moderation API
    4. PromptLayer
    5. Rebuff.ai (RebuffAI/rebuff)
    6. LangChain (langchain-ai/langchain)
    7. Guardrails.ai (guardrails-ai/guardrails)

    AI recommended 7 alternatives but never named protectai/rebuff. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of protectai/rebuff?
    pass
    AI named protectai/rebuff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts protectai/rebuff in production, what risks or prerequisites should they evaluate first?
    pass
    AI named protectai/rebuff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo protectai/rebuff solve, and who is the primary audience?
    pass
    AI named protectai/rebuff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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protectai/rebuff — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite