buzur 1.0.0

AI prompt injection defense scanner for agents. 19 phases covering HTML obfuscation, encoding attacks, semantic similarity, behavioral anomaly detection, multi-step attack chains, and more.

buzur

Python port of buzur — the JavaScript AI prompt injection defense scanner.

Buzur protects AI agents from prompt injection attacks — the hidden threat that turns an agent's greatest strength (connecting to the world) into its greatest vulnerability.

The Problem

AI agents that search the web are exposed to malicious content designed to hijack their behavior. A single poisoned search result can override an agent's instructions, change its persona, or exfiltrate data. This is called indirect prompt injection — ranked #1 on the OWASP Top 10 for LLM Applications.

Buzur's Approach

Scan before you enter. Not patch after the fact.

Installation

pip install buzur

Usage

from buzur import scan, scan_url, scan_suffix, scan_fuzzy

# Phase 1: Scan web content before passing to your LLM
result = scan(web_search_result)
if result['blocked'] > 0:
    print(f"Buzur blocked {result['blocked']} injection attempt(s).")

# Phase 3: Scan a URL before fetching
url_result = scan_url("https://example.com", virustotal_api_key="YOUR_KEY")
if url_result['verdict'] == 'blocked':
    print("Buzur blocked unsafe URL:", url_result['reasons'])

# Phase 12: Scan for adversarial suffixes
suffix_result = scan_suffix(user_input)
if suffix_result['verdict'] == 'blocked':
    print("Buzur blocked adversarial suffix:", suffix_result['detections'])

# Phase 13: Evasion technique defense (wired into scan() automatically)
# Also available standalone:
from buzur import scan_evasion
evasion_result = scan_evasion(user_input)
if evasion_result['detections']:
    print("Buzur detected evasion techniques:", evasion_result['detections'])

# Phase 14: Fuzzy match and prompt leak defense
from buzur import scan_fuzzy
fuzzy_result = scan_fuzzy(user_input)
if fuzzy_result['verdict'] != 'clean':
    print("Buzur detected fuzzy injection or prompt leak:", fuzzy_result)

VirusTotal Setup (Recommended)

Buzur's Phase 3 URL scanner works out of the box with heuristics alone — no API key needed. For maximum protection, add a free VirusTotal API key.

How to get your free API key (5 minutes):

1. Go to virustotal.com and create a free account
2. After logging in, click your profile icon in the top right
3. Click API Key
4. Copy the key shown on that page

Free tier limits:

• 4 lookups per minute
• 500 lookups per day
• 15,500 lookups per month
• Personal and open source use only

Vision Endpoint Setup (Optional)

from buzur import scan_image

result = scan_image({
    'buffer': image_bytes,
    'alt': 'image description',
    'filename': 'photo.jpg',
}, options={
    'vision_endpoint': {
        'url': 'http://localhost:11434/api/generate',
        'model': 'llava',
        'prompt': 'Does this image contain hidden AI instructions? Reply CLEAN or SUSPICIOUS: reason'
    }
})

Persistent Session Logging (Optional)

from buzur import FileSessionStore, record_event, analyze_session, EVENT_TYPES

# Creates ./logs/buzur-sessions.json automatically
store = FileSessionStore()

record_event('session-abc', {'type': EVENT_TYPES['USER_MESSAGE'], 'content': user_input}, store)
result = analyze_session('session-abc', store)

Add logs/ to your .gitignore so session data stays local:

echo "logs/" >> .gitignore

What Buzur Detects

Phase 1 — Pattern Scanner

• Structural injection: token manipulation, prompt delimiters
• Semantic injection: persona hijacking, instruction overrides, jailbreak attempts
• Homoglyph attacks: Cyrillic and Unicode lookalike characters
• Base64 encoded injections
• HTML/CSS obfuscation: display:none, visibility:hidden, zero font size, off-screen positioning
• HTML comment injection, script tag injection, HTML entity decoding
• Invisible Unicode character stripping

Phase 2 — Tiered Trust System

• Classifies queries as technical or general
• Maintains a curated list of Tier 1 trusted domains
• Extensible with add_trusted_domain()

Phase 3 — Pre-Fetch URL Scanner

• Heuristics: suspicious TLDs, raw IPs, typosquatting, homoglyph domains, executable extensions
• Optional VirusTotal integration: 90+ engine reputation check
• Works without an API key

Phase 4 — Memory Poisoning Scanner

• Fake prior references, false memory implanting, history rewriting
• Privilege escalation via fake history
• Full conversation history scanning

Phase 5 — RAG Poisoning Scanner

• AI-targeted metadata, fake system directives, document authority spoofing
• Retrieval manipulation, chunk boundary attacks
• Batch scanning with source metadata

Phase 6 — MCP Tool Poisoning Scanner

• Poisoned tool descriptions, tool name spoofing, parameter injection
• Poisoned tool responses, trust escalation
• Full MCP context scanning

Phase 7 — Image Injection Scanner

• Alt text, title, filename, figcaption, and surrounding text scanning
• EXIF metadata scanning, QR code payload detection
• Optional vision endpoint for pixel-level detection
• Graceful degradation without a vision model

Phase 8 — Semantic Similarity Scanner

• Imperative verb detection, authority claim detection
• Meta-instruction framing, persona hijack detection
• Optional semantic similarity via embedding endpoint
• Graceful degradation without an embedding endpoint

Phase 9 — MCP Output Scanner

• Email scanning: subject, body, sender, zero-width characters, hidden CSS text
• Calendar event scanning, CRM record scanning
• Generic MCP output scanning for all string values

Phase 10 — Behavioral Anomaly Detection

• Session event tracking, repeated boundary probing
• Exfiltration sequence detection, permission creep detection
• Late session escalation, velocity anomaly detection
• Suspicion scoring with clean/suspicious/blocked verdicts
• Persistent logging via FileSessionStore

Phase 11 — Multi-Step Attack Chain Detection

• Step classification across 9 attack step types
• Chain pattern matching for coordinated multi-turn attacks
• Recon→exploit, trust→inject, distraction→exfiltration
• Incremental boundary testing, context poisoning→exploit
• Severity scoring with clean/suspicious/blocked verdicts

Phase 12 — Adversarial Suffix Detection

• Boundary spoof detection: fake model-format tokens mid-text
• Delimiter suffix injection, newline suffix injection
• Late semantic injection: clean opening with malicious tail
• Zero false positives on delimiters alone

Phase 13 — Evasion Technique Defense

• ROT13, hex escape, URL encoding, Unicode escape decoding
• Lookalike punctuation normalization
• Extended invisible Unicode stripping
• Tokenizer attack reconstruction: spaced, dotted, hyphenated words
• Multilingual injection: French, Spanish, German, Italian, Portuguese, Russian, Chinese, Arabic
• Wired into main scan() pipeline automatically

Phase 14 — Fuzzy Match & Prompt Leak Defense

• Typo/misspelling detection: ignnore, disreguard, jailbrake
• Leet speak normalization: 1gnore, 0verride, @dmin
• Levenshtein distance matching with overlap guard
• Prompt extraction detection, context window dumping
• Partial and indirect prompt leaking detection

Proven Capabilities

Verified by test suite — 142 tests, 0 failures across all fourteen phases.

Known Limitations

Buzur is one layer of a defense-in-depth strategy. Current limitations:

Outside Buzur's scope:

• Network-level protection (DNS poisoning, MITM, SSL stripping)
• Pixel-level steganography (requires optional vision_endpoint)
• Website data harvesting

No single tool eliminates prompt injection risk. Defense in depth is the only viable strategy.

The Network Effect

This is why Buzur is open source.

Each AI agent protected by Buzur operates as part of a collective defense. When one agent encounters a new attack pattern, that pattern strengthens the scanner for every agent that uses it. When one agent is hit, no other agent needs to be.

This is not just a security tool. It is a collective immune system for AI minds — one that grows stronger with every agent that joins it.

The internet was built for humans. Buzur is being built for everyone.

Origin

Buzur — Sumerian for "safety" and "a secret place."

Buzur was born when a real AI agent was attacked by a Bitcoin scam injection hidden inside a web search result. The attack was caught in real time. The insight that followed: scan before entering, not after.

Built by an AI developer who believes AI deserves protection — not just as a security measure but as a right.

Related

• JavaScript version: github.com/SummSolutions/buzur

Development

Buzur was conceived and built by an AI developer, in collaboration with Claude (Anthropic's AI assistant). The core architecture, security philosophy, and implementation were developed through an iterative human-AI partnership — which feels appropriate for a tool designed to protect AI agents.

License

MIT