Abstract
Email remains the primary communication channel for enterprises and continues to be the most exploited attack vector for cyber threats. Modern email attacks increasingly rely on impersonation, social engineering, and contextual manipulation rather than traditional malware or malicious links, allowing them to evade existing detection mechanisms. At the same time, email security systems are adopting large language models (LLMs) to improve intent detection and contextual analysis, introducing new risks where email content itself can manipulate or degrade automated reasoning.
The proposal introduces a secure, trust-aware, and LLM-safe framework for automated email threat detection and remediation. The proposed system integrates sender trust evaluation, content safety controls, LLM-based multi-attack analysis, and policy-driven remediation into a unified pipeline. It not only detects a broad range of email-based attacks, including impersonation, business email compromise, phishing, scams, and social engineering, but also protects automated language-model analysis from adversarial manipulation such as prompt injection embedded within emails.
The framework first establishes trust in the sender environment using cryptographically verifiable attestation indicators, enabling reliable detection of impostor and supply-chain attacks that pass traditional authentication checks. It then neutralizes adversarial or machine-targeted content before applying LLM-based reasoning, ensuring that automated analysis remains reliable and explainable. A structured evidence bundle is used to guide the LLM’s reasoning, producing transparent, evidence-grounded threat classifications.
Unlike conventional email security systems that rely primarily on blocking or quarantining messages, the proposed approach enables controlled remediation. Suspicious emails can be safely transformed through sanitization, defanging, attachment gating, and policy-bounded rewriting, allowing legitimate communication to continue while reducing risk. User-facing explanations and verification guidance further improve security outcomes without disrupting workflows.
By combining sender trust, LLM safety, semantic threat detection, and explainable remediation into a single architecture, the proposal addresses critical gaps in current email security solutions. It enables organizations to safely adopt LLM-driven automation while significantly improving resilience against modern email-based attacks.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
M M, Niranjan, "Method for Trust-Aware and Language Model–Driven Email Threat Detection and Mitigation", Technical Disclosure Commons, (July 01, 2026)
https://www.tdcommons.org/dpubs_series/10786