Semantic API Security

100% Open Source

Everything is open source, not just a sdk to a paywall endpoint. no fine prints.

Built for LLM security teams

We help early adopters harness the power of LLMs with enterprise grade security best practices

Fine grained policies

Controlled in realtime using the security team focused dashboard

Data protection first

Your sensitive data never leaves your premise

Zero latency impact

Out of the critical path overhead - by design, so everything is completely async

LLM Agents Governance

Blazing fast Big Data Analytics Dashboard to keep track of your LLM application usage

Prompt injections involve bypassing filters or manipulating the LLM using carefully crafted prompts that make the model ignore previous instructions or perform unintended actions. These vulnerabilities can lead to unintended consequences, including data leakage, unauthorized access, or other security breaches.

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution.

Attackers cause resource-heavy operations on LLMs, leading to service degradation or high costs. The vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

LLMs may inadvertently reveal confidential data in its responses, leading to unauthorized data access, privacy violations, and security breaches. It’s crucial to implement data sanitization and strict user policies to mitigate this.

LLM plugins can have insecure inputs and insufficient access control. This lack of application control makes them easier to exploit and can result in consequences like remote code execution.

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from excessive functionality, permissions, or autonomy granted to the LLM-based systems.