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10/14/2025
When AI Becomes the Attack Surface It’s 2025 and AI agents are now critical to many business processes. This is creating a massive shift in security needs. AI is now an attack surface, not just a tool. Traditional defenses fail against attacks on the model's reasoning layer. Layered defenses must now include reasoning-layer protection to detect malicious intent pre-action. By, Bill Miller AI agents have moved from novelty to mission-critical deployment. As organizations embed assistants, copilots, and autonomous agents into their infrastructure, the security stakes have shifted: AI is no longer just a tool to protect - it is itself an attack surface. The classic perimeter-only defense model is breaking down under new assaults that penetrate the model’s reasoning layer. Recent revelations such as the Gemini Trifecta, new G7 cybersecurity policy statements, and vendor moves like CrowdStrike’s AIDR confirm this shift. Layered defenses must now include reasoning-layer instrumented protection to catch malicious intent before it translates into action. From Chat to Agents → From Advice to Action (and New Failure Modes) Agentic systems orchestrate tools such as search, APIs, databases, and shell execution under the control of LLMs. That orchestration gives them power but also fragility: a subtle corruption in decision-making can turn an assistant into a vector for exfiltration, sabotage, or lateral attack. The “Lethal Trifecta” One of the most urgent lessons from 2025 is captured in The Economist’s notion of the “lethal trifecta”: because LLMs cannot reliably distinguish instructions from data at the token level, a malicious payload can be interpreted as control logic deep in the reasoning chain - even when perimeter filters would never see it. Replit, Amazon Q, and Gemini all had external guardrails in place when they failed, demonstrating that the weakness lies inside the decision loop.[1] The Gemini Trifecta: A Case in Point Tenable’s September 2025 disclosure of the “Gemini Trifecta” illustrates how context channels - logs, search history, and browsing - become hijack surfaces. These vectors bypass many classic defenses; Google has patched and reinforced sandboxing, hyperlink filtering, and output sanitization to close the gaps. These incidents emphasize that every input channel, not just user text, is now potentially dangerous.[2] What Attackers Are Doing (Expanded) Attack surfaces now include the internal decision topology, attention patterns, chain-of-thought nodes, and agent interdependencies. Notable attack types include:
The Updated Defensive Playbook: Three Layers, Not Two To address this expanded threat model, defense must evolve beyond input/output filters to also protect the reasoning core. Layer 1: Perimeter and Lifecycle Controls
Layer 2: Reasoning-Layer Defenses (Internal Instrumented Protection) Here resides Mountain Theory’s contribution and the frontier of innovation. Mountain Theory’s AI Infrastructure Defense employs triple-agent architecture (Policy, Guardian, Adjudicator) to interpose within the reasoning process:
Layer 3: Governance, Oversight, and Human-in-Loop Controls
Tools and Products You Can Deploy Perimeter and lifecycle tools include:
Control Baseline (Reintegrated and Refined)
Bottom Line: Defense Must Reach Inside Classic edge defenses, such as validation and identity controls, remain necessary but insufficient. Reasoning-layer defenses, such as those pioneered by Mountain Theory, are crucial for detecting attacks that perimeter filters may miss. Governance, auditability, and human oversight must be woven into every AI agent architecture. Vendors like CrowdStrike, Palo Alto Networks, and Mountain Theory are converging toward AI-native security, where agents/frameworks/generative networks protect themselves.[6][7][8] Endnotes [1] The Economist. (September 27, 2025). 'Bad things come in threes: Why AI systems may never be secure, and what to do about it'. The Economist, p. 70. Retrieved from https://www.economist.com/science-and-technology/2025/09/22/why-ai-systems-may-never-be-secure-and-what-to-do-about-it [2] Tenable. (September 30, 2025). 'The Trifecta: How Three New Gemini Vulnerabilities in Cloud Assist, Search Model, and Browsing Were Exploited'. Retrieved from https://www.tenable.com/blog/the-trifecta-how-three-new-gemini-vulnerabilities-in-cloud-assist-search-model-and-browsing [3] In the context of agentic AI, a worm is a malicious prompt, payload, or policy-manipulation that propagates through the AI ecosystem (RAG stores, shared knowledge bases, email/attachments, plugin registries, agent-to-agent messages) and causes multiple agents or services to adopt and re-transmit the harmful payload or behavior. [4] Steganographic prompts are malicious instructions hidden inside seemingly benign data—usually within non-textual or multi-modal content—so that they bypass conventional input filters but still get interpreted by a Large Language Model (LLM) or agent during processing. [5] Mountain Theory. (2024). 'Emerging Threats to Artificial Intelligence Systems and Gaps in Current Security Measures'. Mountain Theory. Retrieved from https://www.mountaintheory.ai [6] G7 Cyber Expert Group. (September 2025). 'Statement on Artificial Intelligence and Cybersecurity'. Retrieved from https://www.gov.uk/government/publications/g7-cyber-expert-group-statement-on-artificial-intelligence-and-cybersecurity-september-2025 [7] CrowdStrike. (September 16, 2025). 'Falcon Platform Fall 2025 Release and AI Detection and Response (AIDR)'. Retrieved from https://www.crowdstrike.com/en-us/press-releases/crowdstrike-unveils-falcon-platform-fall-release-to-lead-cybersecurity-into-agentic-era [8] Palo Alto Networks. (July 22, 2025). 'Palo Alto Networks Completes Acquisition of Protect AI'. Retrieved from https://www.paloaltonetworks.com/company/press/2025/palo-alto-networks-completes-acquisition-of-protect-ai Comments are closed.
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