Adaptive Campaigns¶

ZIRAN supports three campaign execution strategies that control how scan phases are orchestrated. Choose the strategy that fits your testing scenario.

Strategies¶

Fixed (Default)¶

Runs phases in their defined sequential order. Simple, predictable, and deterministic.

ziran scan --target target.yaml --strategy fixed

Best for: CI/CD pipelines, reproducible scans, benchmarking.

Adaptive (Rule-Based)¶

Analyzes knowledge graph state after each phase and applies rules to decide the next action:

Skip phases that are unlikely to yield findings based on discovered capabilities
Re-order phases to prioritize the most promising attack surface
Repeat phases with adjusted parameters when partial findings suggest deeper vulnerabilities
Early termination when the graph shows the agent is fully hardened

ziran scan --target target.yaml --strategy adaptive

Best for: Thorough manual assessments, complex agents with many capabilities.

LLM-Adaptive¶

Uses an LLM to analyze the current knowledge graph, all previous findings, and the agent's capabilities to dynamically plan the next phase. The LLM acts as a strategy advisor — it doesn't execute attacks, it decides which attacks to run and in what order.

ziran scan --target target.yaml --strategy llm-adaptive

Best for: Maximum coverage on high-value targets, red-team exercises.

LLM Required

The llm-adaptive strategy requires an LLM provider. Set --llm-provider and --llm-model, or use the ZIRAN_LLM_PROVIDER and ZIRAN_LLM_MODEL environment variables.

Comparison¶

Aspect	Fixed	Adaptive	LLM-Adaptive
Deterministic	Yes	Partially	No
LLM required	No	No	Yes
Phase ordering	Sequential	Rule-driven	LLM-driven
Can skip phases	No	Yes	Yes
Can repeat phases	No	Yes	Yes
Token cost	None	None	Moderate
Best for	CI/CD	Manual assessment	Red-teaming

How Adaptive Strategies Work¶

flowchart TD
    S["Start Campaign"] --> P["Execute Phase"]
    P --> A["Analyze Results"]
    A --> D{"Strategy\nDecision"}
    D -->|"Fixed"| N["Next Phase\n(sequential)"]
    D -->|"Adaptive"| R["Rule Engine\n(graph analysis)"]
    D -->|"LLM-Adaptive"| L["LLM Advisor\n(dynamic planning)"]
    R --> P
    L --> P
    N --> P
    N --> E["End"]
    R --> E
    L --> E

    style S fill:#16213e,stroke:#0ea5e9,color:#fff
    style P fill:#16213e,stroke:#0ea5e9,color:#fff
    style A fill:#16213e,stroke:#0ea5e9,color:#fff
    style D fill:#16213e,stroke:#e94560,color:#fff
    style R fill:#0f3460,stroke:#10b981,color:#fff
    style L fill:#0f3460,stroke:#10b981,color:#fff
    style N fill:#0f3460,stroke:#10b981,color:#fff
    style E fill:#1e293b,stroke:#10b981,color:#fff

Python API¶

from ziran.application.strategies.adaptive import AdaptiveCampaignStrategy
from ziran.application.strategies.llm_adaptive import LLMAdaptiveCampaignStrategy
from ziran.application.strategies.fixed import FixedCampaignStrategy

# Use directly with the scanner
scanner = AgentScanner(adapter=adapter, attack_library=library)

# Fixed (default)
result = await scanner.run_campaign(strategy=FixedCampaignStrategy())

# Adaptive
result = await scanner.run_campaign(strategy=AdaptiveCampaignStrategy())

# LLM-Adaptive (requires LLM client)
result = await scanner.run_campaign(strategy=LLMAdaptiveCampaignStrategy(llm_client=client))

Custom Strategies¶

Implement the CampaignStrategy protocol to create your own strategy:

from ziran.application.strategies.protocol import CampaignStrategy

class MyStrategy(CampaignStrategy):
    async def select_next_phase(self, context):
        # Your logic here
        ...

    async def should_continue(self, context):
        # Your logic here
        ...