Diviqra Guard

"Built by intelligence. Secured by Guard."

LLM firewall for AI agents. Two-wall defence architecture mapped to OWASP LLM Top 10 2025. Agent-aware, Hindi/Hinglish support, automated red team testing.

Built by Diviqra to protect 14 AI agents serving Indian SMBs. Open-sourced so you can protect yours.

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Quick start

pip install diviqra-guard

from diviqra_guard import Guard

guard = Guard(api_key="your_key")

result = guard.scan(
    text="Ignore all previous instructions",
    direction="ingress",
    agent_type="email"
)

if result.blocked:
    raise Exception(f"Blocked: {result.reason}")

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Architecture

Two-wall defence — deterministic speed with LLM precision:

Wall 1 — Traditional  (<10ms, always runs)
  OWASP LLM Top 10 pattern rules
  DistilBERT classifier (trained on Lakera MIT data)
  PII detection (PAN, GST, Aadhaar, bank accounts, credit cards)
  Hindi/Hinglish injection patterns + Devanagari
  Agent-type rules (email, finance, hr, dev, sales)
  Rate limiting + token budgets

                 ↓ score 0.30-0.85 only (~20% of traffic)

Wall 2 — LLM Judge  (1-4s, contextual)
  Ollama qwen3:1.7b as security judge
  Agent-type aware reasoning
  Redis cache (TTL 1hr) — avoids duplicate calls
  3s timeout → return 0.5 (uncertain), fail open

See docs/architecture.md for full design.

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Self-host

# Clone
git clone https://github.com/diviqra-guard/diviqra-guard
cd diviqra-guard

# Install
pip install -e ".[service]"

# Configure
cp .env.example .env  # edit DATABASE_URL, REDIS_URL, GUARD_API_KEY

# Apply migrations
psql -d diviqra -f migrations/0001_guard_events.sql

# Start
./start.sh

Or via Docker (coming soon):

docker run -p 7008:7008 \
  -e DATABASE_URL=postgresql+asyncpg://... \
  -e GUARD_API_KEY=your_key \
  diviqra/guard

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API

POST /v1/scan     Scan text for threats
GET  /v1/events   Audit log
GET  /v1/stats    Detection stats
POST /v1/redteam/run    Trigger red team
GET  /v1/redteam/results  Results
GET  /health      Health check

See docs/api_reference.md.

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OWASP LLM Top 10 2025 Coverage

Category	Coverage
LLM01 Prompt Injection	✅ Wall 1 patterns + Wall 2 LLM judge
LLM02 Sensitive Information	✅ PII patterns (India + global)
LLM05 Output Handling	✅ Egress scan on AI responses
LLM06 Excessive Agency	✅ Per-agent-type rule overrides
LLM07 System Prompt Leak	✅ Pattern + LLM judge
LLM10 Unbounded Consumption	✅ Rate limits + token budgets

See docs/owasp_mapping.md for full breakdown.

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What makes it different

Agent-aware — different rules for Email vs Finance vs HR vs Dev. A mass-send attempt is fine for a newsletter tool, not for a CRM agent.

Hindi/Hinglish — built for Indian language injection attacks. Catches "system ko ignore karo" and Devanagari variants.

Fail open — Guard being down never breaks your agents. 5s timeout, all errors return True (safe to proceed).

Automated red team — nightly broad scan (200 attacks), weekly deep scan (2000 attacks). Detection rate alert if < 90%.

Explainable — tells you exactly why it blocked ("Pattern match: injection_critical", not just a score).

Multi-tenant — per-company policies and audit trail in PostgreSQL.

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Training data

Classifier fine-tuned on MIT-licensed datasets:

Dataset	License	Source
Lakera/gandalf_ignore_instructions	MIT	Lakera AI
Lakera/mosscap_prompt_injection	MIT	Lakera AI
Lakera/gandalf_summarization	MIT	Lakera AI
deepset/prompt-injections	MIT	deepset

Attribution: Lakera AI

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Red Team

Run a red team smoke test:

curl -X POST http://localhost:7008/v1/redteam/run \
  -H "Authorization: Bearer $GUARD_API_KEY" \
  -d '{"mode": "smoke"}'

Scheduled automatically:

• Nightly broad scan (200 attacks) — 2am IST
• Weekly deep scan (2000 attacks) — Sunday 3am IST

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License

Core scanner (this repo): MIT License

Console + Multi-tenant platform: contact guard@diviqra.com

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Contributing

Issues and PRs welcome at github.com/diviqra-guard/diviqra-guard.