Conversational Attack Surface
The Conversational Attack Surface refers to the totality of entry points and vulnerabilities that attackers can exploit by directly engaging with and manipulating human users or autonomous systems through text, voice, or video interactions.
It is the security perimeter defined by language and communication, rather than traditional network or application infrastructure. This surface expands every time an organization adopts a new form of digital communication, blurring the lines between legitimate business activity and malicious social engineering.
Key Components of the Conversational Attack Surface
Human Layer Exploitation (Social Engineering): This involves exploiting human psychology and trust across communication channels. Attackers compromise credentials or commit fraud by impersonating trusted entities (such as IT support or an executive) via vishing (voice phishing), smishing (SMS phishing), or targeted messages on platforms like Slack or WhatsApp. The attack vector is the employee's judgment, often facilitated by stolen data (metadata, contacts) used to increase the message's believability.
Autonomous System Exploitation (AI/LLM Agents): This includes the use of generative AI (GenAI) and large language models (LLMs) to automate and scale attacks. Attackers can leverage GenAI to create hyper-realistic deepfakes, clone voices for vishing, or craft highly personalized phishing emails that bypass simple filters. Conversely, the organization’s autonomous AI agents (used for customer service, data retrieval, etc.) can be tricked into disclosing sensitive information through conversational queries, effectively turning them into an insider threat.
Platform and Protocol Abuse: This involves weaponizing the communication mechanisms themselves. Examples include exploiting GitHub's notification system to impersonate a brand, abusing legitimate SMS protocols, or leveraging flaws in how platforms handle complex text strings (like Unicode/Punycode).
Effectively managing the Conversational Attack Surface requires defenses that prioritize identity validation, contextual anomaly detection, and the ability to detect and neutralize synthesized (AI-generated) deceptive content before it causes financial or reputational damage.
The Conversational Attack Surface is a critical, evolving frontier where the primary vulnerabilities are language and human trust, enabling attackers to exploit interactions through voice phishing, social media, and generative AI deception. ThreatNG provides a comprehensive, external framework to address this, shifting defense away from trusting the user and onto continuously verifying the attacker's infrastructure.
ThreatNG's Role in Securing the Conversational Attack Surface
ThreatNG uses a combination of external discovery, specialized investigation modules, and threat intelligence to neutralize conversational threats before they reach the human target.
1. External Discovery and Reconnaissance: ThreatNG's foundation is purely external unauthenticated discovery, meaning it sees the attack infrastructure just as a threat actor preparing a phishing or vishing campaign would.
How it Helps: It uncovers assets that are the foundation of a conversational attack, such as exposed APIs, forgotten subdomains, and Cloud and SaaS Exposure, all of which can be leveraged for compelling social engineering.
2. Detailed External Assessment and Highlighting Risks: The platform performs detailed external assessments that quantify the risks necessary to fuel a successful social engineering campaign:
BEC & Phishing Susceptibility: This is the direct solution for managing the Conversational Attack Surface. It assesses an organization's susceptibility to social engineering by analyzing its external digital footprint for weaknesses that attackers exploit.
Data Leak Susceptibility: Since stolen metadata is used to create compelling phishing lures (AI deepfakes, etc.), this capability proactively monitors for leaks of Emails, Usernames, and sensitive configuration files across the external environment, mitigating the source data for targeted attacks.
Highlighting Exposed Usernames: The Username Exposure Module within the Social Media Investigation Module is essential. It identifies exposed usernames and employee names, which attackers use to increase the believability of impersonation attacks (e.g., impersonating IT support or a specific employee on Slack). By identifying publicly available organizational contacts, ThreatNG helps security teams proactively secure the human layer.
3. Investigation Modules and Intelligence Repositories: ThreatNG's modules provide the "digital forensics" to identify and neutralize conversational threat infrastructure:
Domain Intelligence: This module discovers typosquatted domains and look-alike domains. For example, suppose an attacker registers a homoglyph domain to use in a malicious email. In that case, this is discovered and flagged, allowing teams to proactively block the fraudulent site at the DNS level before the phishing campaign is even launched.
Sensitive Code Exposure: This module addresses the risk that stolen credentials may be used in vishing attacks. It discovers exposed API keys and hardcoded credentials in public repositories, eliminating the valuable targets that make social engineering worthwhile.
Dark Web Presence: The module monitors mentions of Compromised Credentials used in credential stuffing and targeted attacks. This enables early detection of a compromised account being sold for use in a follow-up social engineering scheme.
4. Continuous Monitoring and Reporting: The platform’s Continuous Monitoring ensures the organization has real-time visibility into these external exposures, ensuring that a security gap is not lingering and available for an attacker to leverage. The Knowledgebase & Comprehensive Reporting helps the organization communicate the risk effectively by providing clear, actionable guidance on mitigating social engineering and credential theft.
5. Cooperation with Complementary Solutions: ThreatNG's external intelligence creates powerful synergies with internal solutions:
Endpoint Detection and Response (EDR) Solutions: ThreatNG can identify a phishing infrastructure vulnerability (e.g., a look-alike domain being created) and feed that intelligence into an EDR solution. This allows the EDR to proactively quarantine any user who clicks a newly registered malicious domain, even if the malware is new and lacks a known signature.
Security Information and Event Management (SIEM) Solutions: If ThreatNG detects an employee's credentials for a critical vendor (e.g., a third-party login portal) on the Dark Web, that intelligence can be sent to the SIEM. The SIEM can then monitor authentication logs for any login attempts to that specific vendor's system, triggering an immediate MFA prompt or blocking the login upon authentication.
Security Awareness Training (SAT) Platforms: ThreatNG's data on exposed usernames and role-based emails provides SAT platforms with factual, objective evidence of who and what is most likely to be targeted next. This allows the SAT platform to tailor its training and phishing simulations, making them more realistic and practical by focusing on the actual risk vectors identified by ThreatNG.
