Mava — agentic threat model

Not certain from the listing — The underlying LLMs are undisclosed. However, because the agent auto-responds to public channel messages, it is highly susceptible to indirect prompt injection, adversarial inputs, and output hijacking that could deface public brand channels.

Mava automatically connects existing content to train its AI knowledge base. This creates a risk of knowledge-base poisoning if an attacker can manipulate the connected source documents, as well as data exfiltration risks if sensitive customer PII is ingested into the shared team inbox.

Not certain from the listing — The orchestration framework is proprietary. The primary threat at this layer is insecure tool integration, specifically how the agent parses and routes incoming API payloads from Slack, Discord, and Telegram without executing malicious commands.

Not certain from the listing — Hosting and infrastructure details are omitted. A key threat is the exposure of sensitive API tokens and webhooks used to authenticate Mava to external platforms like Discord, Slack, and Telegram.

Mava provides analytics for support operations, but the listing does not specify real-time LLM guardrails or anomaly detection. This creates a blind spot where abusive or manipulative user interactions in public channels may go undetected until reported.

Not certain from the listing — Compliance certifications (e.g., GDPR, SOC2) and access control mechanisms for the 'shared team inbox' are not detailed. Unauthorized access to this inbox could compromise consolidated customer support data across all channels.

Mava acts as a bridge across multiple external ecosystems (Slack, Telegram, Discord, Email). A compromise of the Mava platform could lead to cascading security failures, allowing an attacker to broadcast malicious messages or phishing links across all connected organizational channels simultaneously.