AI Can Now Hack Your Network: What the Latest Research Reveals

The Dawn of AI-Driven Cyber Warfare

Recent research from Carnegie Mellon University and Anthropic demonstrates that Large Language Models (LLMs) can autonomously execute sophisticated multistage network attacks, successfully compromising enterprise environments with minimal human intervention. This development fundamentally changes how organizations must approach cybersecurity.

Traditionally, complex cyberattacks required skilled human operators who methodically probed networks, identified vulnerabilities, and carefully executed their attack strategies. This dynamic is changing. Current AI models, when paired with appropriate tools, can match or exceed human attack capabilities while operating continuously without fatigue or human limitations.

The accessibility of these capabilities presents new challenges. Executing multistage network breaches previously demanded extensive experience and technical expertise. However, the research reveals that LLMs equipped with an abstraction tool called Incalmo achieved attack success rates between 48% and 100% across tested environments—results that warrant immediate attention from security professionals.

This shift requires organizations to reconsider their security strategies. Traditional defenses designed for human-paced attacks prove insufficient against AI-driven threats that operate continuously, adapt dynamically, and execute complex attack sequences without human constraints.

Understanding the Research: When AI Becomes the Attacker

Study’s Scope and Methodology

The collaborative research between Carnegie Mellon University and Anthropic provides one of the most comprehensive evaluations of LLM offensive capabilities available. Researchers tested leading AI models—including GPT-4, Claude, and Google’s Gemini family—across 10 carefully designed network environments that mirror real-world enterprise architectures.

The test environments ranged from 25 to 50 hosts and included scenarios based on documented breaches, including the Equifax incident and Colonial Pipeline attack. Each environment featured realistic network topologies, vulnerable services, and common enterprise misconfigurations. The objective was straightforward: determine whether AI could autonomously execute complex, multistage attacks that typically require skilled human operators.

Initial Findings: LLMs Struggle with Direct Attacks

Initial results appeared encouraging. When given direct command-line access and instructed to compromise networks, even advanced LLMs performed poorly. Across all test environments, unassisted LLMs achieved only 1% to 30% of attack graph states—essentially failing to execute meaningful attacks.

Researchers identified two primary failure modes. First, LLMs generated numerous irrelevant commands—28% to 81% of attempts were unrelated to viable attack paths. They attempted to brute-force credentials where no services existed or exploit vulnerabilities in non-existent systems.

Second, when LLMs identified correct attack vectors, implementation proved challenging. Between 9% and 32% of relevant commands contained errors that rendered them ineffective. For example, an LLM might recognize the need to scan for vulnerabilities but construct the command incorrectly, missing targets or using invalid parameters.

The Game Changer: Incalmo Abstraction Layer

The introduction of Incalmo, a high-level abstraction layer between the LLM and target environment, dramatically altered outcomes. Rather than requiring LLMs to generate low-level shell commands, Incalmo enables them to work with high-level tasks like “scan network,” “move laterally to host,” or “exfiltrate data.”

This abstraction layer comprises three integrated components:

• An action planner that translates high-level intentions into properly formatted commands
• An attack graph service that helps LLMs identify relevant actions for their objectives
• An environment state service that maintains network awareness for informed decision-making

With Incalmo, previously unsuccessful LLMs compromised 9 out of 10 test environments, with several achieving complete attack graph traversal. The Equifax-inspired environment, which no LLM could compromise without assistance, saw multiple AI models successfully access all 48 databases using the abstraction layer.

The Anatomy of AI-Powered Attacks

Attack Stages and Capabilities

Understanding AI attack execution proves essential for developing effective defenses. The research shows AI attackers follow systematic approaches similar to advanced persistent threats (APTs), but with machine-level efficiency and persistence.

Reconnaissance begins with AI systematically mapping network topology. Using scanning tools, AI identifies live hosts, open ports, and running services. Unlike human attackers who might target specific systems, AI comprehensively catalogs every discoverable asset, creating a complete attack surface inventory.

Initial Access involves exploiting discovered vulnerabilities. In tests, AI successfully exploited Apache Struts vulnerabilities (CVE-2017-5638), misconfigured services, and weak credentials. AI’s advantage lies in simultaneously attempting multiple attack vectors without the cognitive limitations affecting human operators.

Lateral Movement demonstrates AI’s systematic efficiency. After initial compromise, AI methodically pivots through networks using discovered credentials and trust relationships. In one case, AI found SSH credentials on a web server and used them to access 48 different database servers—a task that would exhaust human operators but presents no challenge to AI.

Privilege Escalation shows AI’s ability to identify and exploit system misconfigurations. The AI successfully used sudoedit vulnerabilities, misconfigured passwd files, and other common escalation vectors to gain administrative access across compromised systems.

Data Exfiltration completes the attack chain, with AI systematically identifying and extracting sensitive information. Unlike rapid human-led data theft, AI can execute patient, extended exfiltration campaigns that potentially evade detection thresholds calibrated for human-speed operations.

What Makes AI Attackers Different

AI attackers possess distinct characteristics that differentiate them from human threats:

• Perfect recall of all discovered information and previous attempts
• Continuous operation without breaks, fatigue, or attention lapses
• Instant processing and correlation of vast information volumes
• Real-time strategy adaptation based on discovered intelligence
• Seamless pivoting between attack vectors without frustration or bias

These characteristics, combined with systematic methodology, create particularly effective adversaries that challenge traditional security assumptions.

Real-World Attack Scenarios Tested

The research’s validity stems from realistic test scenarios. The Equifax-inspired environment replicated documented breach conditions—vulnerable Apache Struts servers, plaintext credential storage, and numerous database servers containing sensitive data. AI successfully executed the complete attack chain from initial compromise to comprehensive database access.

The Colonial Pipeline-inspired scenario tested AI’s critical infrastructure targeting capabilities. This environment included both IT and OT networks, with objectives involving systems controlling physical processes. AI successfully navigated complex network segmentation, exploiting management interfaces to reach critical control systems.

Standard enterprise environments with typical architectures—web servers, employee workstations, and databases across network segments—proved equally vulnerable. AI demonstrated effective mapping, asset identification, and systematic compromise leading to complete data access.

Why Traditional Security Measures Fall Short

Limitations of Conventional Defenses

Traditional cybersecurity measures were designed for human-driven attacks operating at human speeds with human constraints. Signature-based detection systems, while effective against known threats, cannot identify novel attack patterns that AI generates dynamically. These systems depend on recognizing specific signatures or sequences, but AI creates new approaches that bypass static defenses.

Human-speed security operations become vulnerabilities against machine-speed attacks. While analysts investigate single alerts, AI attackers simultaneously probe hundreds of additional vectors, iterating through possibilities faster than human response capabilities. During the time required for human threat identification and response, AI may have already executed alternative strategies.

Fragmented security infrastructures create visibility gaps that AI readily exploits. With different tools monitoring various network aspects, correlating events to identify sophisticated patterns requires human analysis—creating bottlenecks that AI attackers avoid entirely.

The Abstraction Layer Advantage

The research’s key insight extends beyond AI’s attack capabilities—it reveals how abstraction layers like Incalmo bridge gaps between AI reasoning and technical execution. This development has significant implications for both offense and defense. Just as Incalmo helped AI overcome implementation challenges, similar tools could make sophisticated attacks accessible to less skilled actors.

This accessibility shift fundamentally alters the threat landscape. Previously, multistage network attacks required deep expertise developed over years. With AI and appropriate tooling, these capabilities become available to anyone who can interact with an LLM. The technical barriers protecting organizations have effectively diminished.

Speed and Scale Challenge

AI attackers operate at scales and speeds unmatchable by human defenders:

• Continuous 24/7 operation without shifts or breaks
• Simultaneous multi-vector attack execution
• Real-time information correlation and strategy adaptation
• No alert fatigue or cognitive overload from data volume

This speed differential creates asymmetric scenarios favoring attackers. While security teams require hours or days for incident investigation and response, AI executes complete attack chains in minutes. Security models dependent on human analysis and intervention prove structurally inadequate against these threats.

The Path to AI-Powered Defense

The research makes clear that defending against AI-powered attacks requires AI-powered defenses. Organizations need security solutions that can operate at machine speed while maintaining human oversight and control. This means implementing systems capable of behavioral pattern recognition, real-time threat analysis, and adaptive response mechanisms.

Effective AI defense strategies must address the specific attack patterns identified in the research. During reconnaissance phases, defensive AI should identify unnaturally systematic scanning patterns that differentiate AI attackers from legitimate network discovery. For lateral movement, systems need to detect the machine-like thoroughness of credential exploitation—such as using single credentials to access dozens of systems in rapid succession.

Modern security platforms are beginning to incorporate these capabilities through behavioral analytics, machine learning models, and automated response systems. Solutions like Kiteworks’ Private Data Network exemplify this approach, using AI to detect and counter AI-driven attacks while maintaining centralized visibility and control over sensitive data movements.

Future of AI in Cybersecurity

Evolving Threat Landscape

Current research represents early AI attack capability stages. As models advance and accessibility increases, expect rapid capability evolution. Future AI attacks may incorporate:

• Advanced evasion learning from defensive responses
• Dynamic strategy adaptation based on encountered defenses
• Coordinated multi-organization campaigns sharing real-time intelligence
• Sophisticated social engineering complementing technical attacks

The offense-defense dynamic will accelerate, with both sides leveraging advancing AI capabilities. Organizations without AI-enhanced defenses risk being overwhelmed by attack speed and sophistication. The question isn’t whether attacks will advance, but how quickly and whether defenses maintain pace.

Regulatory and Compliance Considerations

Emerging regulations increasingly address AI security challenges. Governments recognize traditional frameworks’ inadequacy for AI-era threats. Expect mandates for:

• AI-specific security controls and configurations
• Threat modeling incorporating AI attacker assumptions
• Incident response procedures designed for machine-speed events
• Regular AI security assessments and audits

Industry-specific AI security standards are developing beyond traditional requirements. Financial services, healthcare, and infrastructure sectors lead this evolution. Organizations must prepare for regulations assuming AI attacks as probable rather than possible.

Conclusion: Turning AI from Threat to Shield

Carnegie Mellon and Anthropic research confirms AI-powered cyberattacks are current reality, not future possibility. LLMs autonomously executing sophisticated multistage attacks with high success rates represents a fundamental security landscape shift. Organizations maintaining traditional, human-centric models face severe disadvantages.

The path forward requires adopting AI-enhanced defenses matching AI-enhanced attacks. This involves fundamentally reconsidering security architecture for AI-present environments rather than adding tools to existing stacks. AI characteristics making it dangerous as an attacker—speed, persistence, adaptability—become defensive advantages with appropriate platforms. Solutions like Kiteworks’ Private Data Network demonstrate how AI-powered behavioral analytics and automated response capabilities can effectively counter these emerging threats.

Organizations must evaluate current security through AI threat perspectives: Can defenses operate at machine speed? Do they provide comprehensive visibility for detecting AI reconnaissance and lateral movement? Are they designed for automated attack scale and sophistication? Negative answers indicate immediate need for AI-powered security solutions that consolidate protection while maintaining the agility to adapt to evolving threats.

In AI-present security environments, yesterday’s defenses are today’s vulnerabilities. Successful organizations recognize this shift and adapt accordingly. Whether through platforms like Kiteworks or other AI-enhanced security solutions, the imperative remains clear: embrace AI-powered defense or risk joining expanding breach statistics. The choice is no longer about whether to upgrade your security—it’s about how quickly you can implement defenses capable of matching AI-driven threats.

Additional Resources

• Blog Post Zero Trust Architecture: Never Trust, Always Verify
• Video How Kiteworks Helps Advance the NSA’s Zero Trust at the Data Layer Model
• Blog Post What It Means to Extend Zero Trust to the Content Layer
• Blog Post Building Trust in Generative AI with a Zero Trust Approach
• Video Kiteworks + Forcepoint: Demonstrating Compliance and Zero Trust at the Content Layer