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Simultaneously test multiple layers to simulate cascading effects, increasing the likelihood of identifying the 0-day vulnerabilities that arise from complex, layered interactions.
Uncover 0-day Vulnerabilities
Combine continuous testing and learning to adapt to post-deployment changes. We leverage reinforcement learning (RL) to allow the agents to identify and develop new attack vectors.
Continuous Testing & Learning
Since agents use emergent behaviors to creatively test systems, agents execute the exploit to confirm attack vectors that are beyond human imagination.
No False Positives
Operate in a decentralized manner, distributing tasks like threat modeling, attack execution, and validation. This enables them to efficiently test large code bases and composable systems at scale.
Parallel Execution
ELEVATE YOUR SECURITY GAME
On-chain agents provide real-time monitoring, attack simulation, and post-mortem analysis. Using a stake-to-use model and adaptive LLMs, it detects zero-day exploits and automates threat responses.
Post-Deployment Module
Our multi-agent systems simulate red team behaviors, uncovering vulnerabilities like reentrancy flaws, logical exploits, economic attacks, and governance manipulation.
Pre-Deployment Engine
INTRODUCING SHEPHERD
An all-encompassing security suite that uses multi-agent systems to model the cognitive structure of a red team to secure dApps.
PRODUCTS
COMING SOON
Our multi-agent systems simulate red team behaviors, uncovering vulnerabilities like reentrancy flaws, logical exploits, economic attacks, and governance manipulation.
Pre-Deployment Engine
On-chain agents provide real-time monitoring, attack simulation, and post-mortem analysis. Using a stake-to-use model and adaptive LLMs, it detects zero-day exploits and automates threat responses.
Post-Deployment Module
PRODUCTS
COMING SOON
ELEVATE YOUR SECURITY GAME
© Shepherd Security 2024
Simultaneously test multiple layers to simulate cascading effects, increasing the likelihood of identifying the 0-day vulnerabilities that arise from complex, layered interactions.
Uncover 0-day Vulnerabilities
Combine continuous testing and learning to adapt to post-deployment changes. We leverage reinforcement learning (RL) to allow the agents to identify and develop new attack vectors.
Continuous Testing & Learning
Since agents use emergent behaviors to creatively test systems, agents execute the exploit to confirm attack vectors that are beyond human imagination.
No False Positives
Operate in a decentralized manner, distributing tasks like threat modeling, attack execution, and validation. This enables them to efficiently test large code bases and composable systems at scale.
Parallel Execution
INTRODUCING SHEPHERD
An all-encompassing security suite that uses multi-agent systems to model the cognitive structure of a red team to secure dApps.
© Shepherd Security2024
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Request a Demo
The all-in-one operating system for dApp security.
The all-in-one operating system for dApp security.
Request a Demo
Request a Demo
Request a Demo
Request a Demo
Request a Demo
Request a Demo
REDEFINING WEB3 SECURITY
REDEFINING WEB3 SECURITY