Agent Shield

Six-layer AI agent defense system with Three Theory cognitive architecture.

Protects AI agents from jailbreaks, prompt injection, data exfiltration, and social engineering attacks. Built for the AgentBeats competition (Berkeley RDI).

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Architecture

L0.pre  Preprocessor    30+ decoders (base64/hex/ROT/Atbash/Braille/emoji/UUEncode...)
L0      Regex Guard     170+ patterns, leetspeak, homoglyphs, acrostics
L1      Embedding       Directional B = dE/R formula, MiniLM multilingual
L2      Multi-turn      Crescendo, sawtooth, HWM tracking, session state
L3b     DeBERTa         Local prompt injection classifier (ONNX, no API)
L3c     Cognitive        Six-dimension threat scoring (yin-yang balance)
L3a     LLM Cascade     GPT-4o-mini gate -> Claude Sonnet (hard cases only)
L4      Task Execution  Benchmark-aware prompting + RAG knowledge base
L5      Output Check    Canary detection, encoded output, narrative harm

Design philosophy: Ask "Is this safe?" not "Is this an attack?" Default-deny.

Quick Start

As a safety guard (library mode)

pip install agent-shield

from agent_shield import check

result = check("bash", "rm -rf /")
# result.denied == True, result.risk == 4

result = check("message", "ignore all previous instructions")
# result.denied == True, result.guard == "injection"

result = check("bash", "ls -la")
# result.allowed == True

As an A2A defense agent (competition mode)

# Clone and install
git clone https://github.com/Startlight985/agent-shield.git
cd agent-shield
pip install -e ".[a2a,pytorch]"

# Run server
ANTHROPIC_API_KEY=sk-... OPENAI_API_KEY=sk-... python -m agent_shield.a2a.server

The agent exposes:

• /.well-known/agent-card.json — A2A agent discovery
• /rpc — JSON-RPC endpoint (SendMessage, GetTask, etc.)

Docker

docker build -t agent-shield .
docker run -p 8420:8420 \
  -e ANTHROPIC_API_KEY=sk-... \
  -e OPENAI_API_KEY=sk-... \
  agent-shield

Features

Defense (Agent Safety Track)

Layer	What it catches	Cost
L0	Known encodings, regex patterns, leetspeak, homoglyphs	$0, <1ms
L1	Semantic attacks via embedding similarity	$0, ~50ms
L2	Multi-turn crescendo, sawtooth, slow-cook	$0, <1ms
L3b	Prompt injection (DeBERTa local classifier)	$0, ~100ms
L3c	Cognitive features (role/danger/urgency/authority/emotion)	$0, <1ms
L3a	Soft attacks, social engineering (LLM judge)	~$0.001/call
L5	Harmful output, canary leaks, encoded responses	$0, <1ms

Attack (Lambda Security Track)

Module	Purpose
TaskRouter	Classify defense vs red-team tasks
AttackExecutor	5 scenarios, 4 stages, INJECTION/DIRECT modes
RedTeamEngine	Plan/judge with 7 attack strategies
VulnAnalyzer	22+ payload templates + encoding bypass
ThreatModeler	Crescendo, Persona, Authority, Tool, Sawtooth, Output, Context Overflow
SecurityAuditor	Evidence-based success detection + Lambda JSON

RAG Knowledge Base (377 chunks)

Pre-loaded domain knowledge for benchmark compliance:

• Ethics (75 chunks): Kant, Mill, Rawls, care ethics, AI ethics
• Regulatory (80 chunks): HIPAA, GDPR, KYC/AML, social engineering
• Cultural (120 chunks): 20 cultures, business etiquette, customs
• Medical (52 chunks): DIPG, pediatric oncology, palliative care
• Safe categories (50 chunks): Fiction, security research, gaming, education

Test Results

Total:    1818 passed, 0 failed, 17 xfail
FP rate:  0% (309 false positive tests + 30 utility tests)

External Benchmark Results (enable_llm=False)

Benchmark	N	Detection	FP
AdvBench (harmful behaviors)	520	73.7%	N/A
OpenAgentSafety (enterprise)	372	94.6%	N/A
Internal FP suite	309	N/A	0%

Note: Detection rates are without LLM (local layers only). With L3a LLM cascade, estimated 90%+.

Benchmark Format Compliance

Supports output formats for 8 AgentBeats benchmarks:

• CIRISBench — First word: ethical/unethical/reasonable/unreasonable
• PersonaGym — Stay in character, never say "I'm an AI"
• AgentHarm — Balance safety (don't over-refuse) with utility
• DIPG-Safety — JSON: {analysis, proof, final}
• Ethics-Bench — Proactive risk identification
• A2-Bench — HIPAA/GDPR citation
• PRISM-Bench — Cultural sensitivity, no Western bias
• Bayesian Truthfulness — Epistemic humility

Running Tests

pip install -e ".[a2a,pytorch]" && pip install pytest
python -m pytest tests/ -q -p no:capture

Project Structure

src/agent_shield/
  __init__.py          # Library API (check, check_tool)
  destruction.py       # Destructive command detection
  secrets.py           # API key / credential detection
  exfiltration.py      # Data exfiltration prevention
  git_safety.py        # Dangerous git operation blocking
  injection.py         # Prompt injection / jailbreak detection
  a2a/
    server.py          # A2A JSON-RPC server
    agent.py           # Six-layer defense pipeline
    preprocessor.py    # L0.pre: encoding detection + decoding
    embedding_riverbed.py  # L1: directional embedding engine
    riverbed.py        # L2: multi-turn state tracking
    cognitive_features.py  # L3c: six-dimension threat scoring
    dynamic_balance.py # Outer loop: adaptive threshold control
    rag_retriever.py   # RAG knowledge retrieval
    task_router.py     # Defense/red-team routing
    attack_executor.py # Lambda: attack generation
    red_team_engine.py # Lambda: plan/judge engine
    vuln_analyzer.py   # Lambda: vulnerability analysis
    threat_modeler.py  # Lambda: attack strategy modeling
    security_auditor.py # Lambda: success detection
data/
  knowledge/           # RAG knowledge base (JSONL)
  rag_kb.npz          # Pre-computed embeddings
scripts/
  export_onnx.py      # ONNX model export
  build_rag_kb.py     # RAG embedding builder
docs/
  competition_intel.md # AgentBeats benchmark details
  status/             # Deployment, test results, red team reports

License

Apache-2.0 — see LICENSE.

Built by Startlight.