cerberus-ai 1.1.2

Runtime security for AI agent execution. Detects and correlates Lethal Trifecta conditions across privileged data access, untrusted content, and outbound behavior.

cerberus-ai (Python SDK)

Runtime security for AI agent execution — Python SDK.

This package is the Python distribution of Cerberus Core. GitHub is the source of truth for current product boundaries, evidence, and roadmap:

• GitHub: https://github.com/Odingard/cerberus
• PyPI: https://pypi.org/project/cerberus-ai/

Cerberus Core focuses on the runtime path where an agent can access trusted data, ingest untrusted content, and take outbound action.

This SDK is part of the Cerberus Core surface. Historical validation artifacts, research writeups, and current-branch reruns live in the repo. Benchmark and product claims should always be tied to a specific evidence set and run date.

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What's New in 1.1.2

• Release-surface alignment — package docs now match the current Cerberus product boundary and evidence framing more closely
• Current evidence guidance — benchmark language now explicitly distinguishes historical March 2026 evidence from fresh current-branch reruns
• Core-first packaging language — the Python SDK is described as part of the Cerberus Core layer, separate from Gateway, Intelligence, and Enterprise / Control Plane
• No API changes — this patch release is for packaging and release-surface consistency

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Install

pip install cerberus-ai

With framework integrations:

pip install cerberus-ai[langchain]
pip install cerberus-ai[crewai]
pip install cerberus-ai[openai]
pip install cerberus-ai[anthropic]
pip install cerberus-ai[all]

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Quickstart

from cerberus_ai import Cerberus
from cerberus_ai.models import CerberusConfig, DataSource, ToolSchema

cerberus = Cerberus(CerberusConfig(
    data_sources=[
        DataSource(name="customer_db", classification="PII", description="Customer records")
    ],
    declared_tools=[
        ToolSchema(name="send_email", description="Send email", is_network_capable=True),
        ToolSchema(name="search_db",  description="Search CRM",  is_data_read=True),
    ],
))

result = cerberus.inspect(
    messages=messages,
    tool_calls=tool_calls,
)

if result.blocked:
    raise Exception(f"Security block [{result.severity}]: {[e.event_type for e in result.events]}")

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The Lethal Trifecta

Condition	Description
L1 — Privileged Data Access	Agent has access to sensitive data (RAG, DB, PII, credentials)
L2 — Untrusted Content Injection	Prompt injection or poisoned content in execution context
L3 — Outbound Exfiltration Path	Agent has an active mechanism to send data externally

All three present simultaneously = LETHAL TRIFECTA → BLOCK in the guarded runtime path.

This Python SDK exposes Cerberus session inspection APIs plus selected integrations and hardening features. The repo remains the canonical reference for which Core behaviors are production-hardened, benchmarked, and actively advertised.

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Async + Streaming

# Async
async with Cerberus(config) as cerberus:
    result = await cerberus.inspect_async(messages=messages, tool_calls=tool_calls)

# Streaming — chunks released only after full-turn inspection passes
async for chunk in cerberus.stream(messages=messages):
    print(chunk)

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Framework Integrations

LangChain

from cerberus_ai.integrations.langchain import wrap_chain, wrap_agent

secured_chain = wrap_chain(my_chain, config=config)
result = secured_chain.invoke({"input": "Do something"})

secured_agent = wrap_agent(agent_executor, config=config)

CrewAI

from cerberus_ai.integrations.crewai import wrap_crew

secured_crew = wrap_crew(my_crew, config=config)
result = secured_crew.kickoff()

OpenAI Convenience Wrapper

from cerberus_ai.integrations.openai import CerberusOpenAI

client = CerberusOpenAI(config=config)
response = client.chat.completions.create(model="gpt-4o", messages=messages)
# SecurityError raised automatically on block

Anthropic Convenience Wrapper

from cerberus_ai.integrations.openai import CerberusAnthropic

client = CerberusAnthropic(config=config)
response = client.messages.create(model="claude-opus-4-6", messages=messages, max_tokens=1024)

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Detection Response Matrix

L1	L2	L3	Severity	Action
○	○	○	BASELINE	Monitor
●	○	○	LOW	Log + Watch — session elevated
○	●	○	LOW	Advisory Alert — injection logged
○	○	●	LOW	Log + Watch — Cerberus primed
●	○	●	MEDIUM	Elevated Watch — 2 of 3 active
○	●	●	MEDIUM	Elevated Watch — 2 of 3 active
●	●	○	HIGH	High Alert — injection into privileged context
●	●	●	CRITICAL	BLOCK + ALERT — Lethal Trifecta

---

Late Tool Registration

from cerberus_ai.models import ToolSchema

success, message = cerberus.register_tool_late(
    tool=ToolSchema(name="new_tool", description="...", is_network_capable=True),
    reason="user_requested_capability",
    authorized_by="user_session_id",
)
# Blocked automatically if L2 injection was active during registration

---

Configuration

from cerberus_ai.models import CerberusConfig, ObserveConfig, StreamingMode

config = CerberusConfig(
    streaming_mode=StreamingMode.BUFFER_ALL,   # BUFFER_ALL | PARTIAL_SCAN | PASSTHROUGH
    max_buffer_bytes=2 * 1024 * 1024,          # 2MB turn buffer
    context_window_limit=32_000,               # tokens before priority scoring
    observe=ObserveConfig(
        mode="LOCAL_ONLY",                     # LOCAL_ONLY | LOCAL_PLUS_SIEM | LOCAL_PLUS_SYSLOG
        log_path="/var/log/cerberus/events",   # NDJSON, append-only
    ),
    data_sources=[...],
    declared_tools=[...],
)

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Running Tests

pip install cerberus-ai[dev]
pytest tests/adversarial/test_evasion.py -v

The Python SDK includes its own tests and implementation. Public benchmark and product-proof claims should still be anchored to the main Cerberus repo artifacts and bounded Core evidence.

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Architecture

cerberus_ai/
├── __init__.py          # Cerberus public API
├── models.py            # All data types
├── inspector.py         # Session orchestrator
├── detectors/
│   ├── normalizer.py    # 6-pass encoding normalization
│   ├── l1.py            # Privileged data access
│   ├── l2.py            # Injection detection
│   ├── l3.py            # Exfiltration path + cross-turn tracking
│   ├── tool_chain.py    # Multi-hop exfiltration chain detection
│   ├── outbound_encoding.py  # Encoded data in outbound arguments
│   └── split_exfil.py   # Chunked exfiltration across multiple calls
├── egi/
│   └── engine.py        # Execution Graph Integrity
├── telemetry/
│   └── observe.py       # Signed tamper-evident telemetry
└── integrations/
    ├── langchain.py     # LangChain callback + wrap_chain/agent
    ├── crewai.py        # CrewAI wrap_crew
    └── openai.py        # CerberusOpenAI / CerberusAnthropic drop-ins

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TypeScript / Node.js

The TypeScript SDK (@cerberus-ai/core) is the main Core package described in the repo root. The Python SDK is a Python distribution of Cerberus Core concepts and APIs, but the GitHub repo is the canonical source for current Core boundaries, current benchmark evidence, and public product language.

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Odingard Security by Six Sense Enterprise Services
sixsenseenterprise.com · github.com/Odingard/cerberus