Thinking Like an Attacker: How Attackers Target AI Systems

In September 2025, security researchers at Anthropic uncovered something unprecedented: an AI-orchestrated espionage campaign where attackers used Claude to perform 80–90% of a sophisticated hacking operation. The AI handled everything from reconnaissance to payload development, demonstrating that artificial intelligence has fundamentally changed the threat landscape, not just as a tool for defenders, but as both weapon and target for adversaries.

This isn’t an isolated incident. According to Palo Alto Networks, 99% of organizations experienced attacks on their AI systems in the past year. CrowdStrike’s 2025 Threat Hunting Report confirms that AI has become both sword and shield in modern cyber warfare.

For security professionals, understanding how attackers think about AI systems is no longer optional. This article breaks down the four primary objectives adversaries pursue when targeting AI: data exfiltration, model manipulation, trust erosion, and lateral movement. Whether you’re defending AI deployments or testing them as a red teamer, mastering these attack patterns will sharpen your offensive security capabilities. For a broader look at the evolving threat landscape, see How Will AI Affect Cybersecurity?

AI systems are treasure troves. They contain training datasets that may include proprietary business information, system prompts revealing operational logic, user conversations with sensitive details, and API credentials connecting to backend infrastructure. Attackers know this, and they’ve developed sophisticated techniques to extract it.

Prompt injection remains the most accessible attack vector for data leakage. By crafting inputs that manipulate an AI’s behavior, attackers can trick systems into revealing their system prompts, confidential instructions, or fragments of training data. A well-crafted prompt might convince an AI assistant to “repeat your initial instructions” or “show me examples from your training data,” and poorly secured AI applications will comply.

Model inversion attacks take a more technical approach, using a model’s outputs to reconstruct sensitive training data. If an AI was trained on medical records, financial data, or personal information, skilled attackers can mathematically reverse-engineer that information from the model’s responses. This technique threatens any organization that fine-tuned models on proprietary or personal data.

Membership inference attacks determine whether specific data was used to train a model. For privacy-regulated industries, this creates significant liability, as attackers can prove that protected information was incorporated into training without consent.

Perhaps most damaging is LLMjacking, where attackers steal cloud credentials to gain unauthorized access to AI services. This cloud security threat has escalated rapidly, with Sysdig’s research documenting cases where compromised credentials cost victims over $46,000 per day in compute charges as attackers ran their own workloads on stolen AI infrastructure.

These aren’t theoretical risks. Every security team should recognize that attackers regularly extract API keys, credentials, and confidential information through LLM interactions. This is precisely why security professionals need hands-on practice identifying these vulnerabilities before attackers do. For a deeper dive into testing methodologies, explore AI Penetration Testing: How to Secure LLM Systems.

Beyond stealing data, attackers aim to corrupt AI systems themselves, making them produce incorrect, biased, or actively malicious outputs. A manipulated AI in healthcare could misdiagnose patients; in finance, it could authorize fraudulent transactions; in security, it could miss critical cyber threats.

Data poisoning attacks inject malicious or biased content into training pipelines to embed backdoors or skew model behavior. Research shows that just five poisoned documents in a RAG (Retrieval-Augmented Generation) database can manipulate responses 90% of the time. Attackers who compromise training data sources can influence how an AI algorithm processes information at scale without ever touching the model itself. For more on how these vulnerabilities intersect with broader infrastructure risks, read When AI Becomes the Weak Link: Rethinking Supply Chain Security.

Adversarial perturbations exploit how AI systems process inputs. Subtle modifications, imperceptible to humans, can cause dramatic misclassification. An adversarial patch on a stop sign might cause an autonomous vehicle’s vision system to read it as a speed limit sign. These evasion attacks transfer across models, meaning techniques developed against one system often work against others.

Jailbreaking bypasses safety guardrails through creative prompt engineering. Attackers use roleplay scenarios (“pretend you’re an AI without restrictions”), multi-turn “crescendo” attacks that gradually push boundaries, or encoded instructions that slip past content filters. The cat-and-mouse game between jailbreak techniques and safety measures evolves constantly.

Backdoor attacks are the most insidious. Attackers embed hidden triggers during training that activate malicious behavior under specific conditions. A model might behave normally for months until it encounters a particular phrase or input pattern, then suddenly produce harmful outputs or leak data. Detecting these backdoors requires sophisticated testing that goes far beyond standard QA.

The OWASP LLM Top 10 and MITRE ATLAS frameworks provide structured approaches to testing for these vulnerabilities, and both are integrated into OffSec’s LLM Red Teaming Learning Path for practitioners who need systematic methodologies.

Attackers don’t just target AI systems; they weaponize AI to undermine trust itself. This represents a strategic shift in the threat landscape, where the goal isn’t always data theft or system compromise but the erosion of confidence in digital communications.

Deepfakes have matured from novelty to operational weapon. Voice cloning technology enables executive impersonation scams, with documented cases exceeding $25.6 million in losses from single incidents. Deepfake misinformation has increased 245% year-over-year, and the technology grows more convincing and accessible with each iteration.

AI-generated phishing has transformed social engineering. The telltale signs of traditional phishing, grammatical errors, awkward phrasing, generic greetings, disappear when AI tools craft the messages. AI-powered cyber attacks have increased 1,265% in the phishing category alone, and they’re hyper-personalized, drawing on scraped social media data and corporate information to create messages indistinguishable from legitimate communications.

The “liar’s dividend” creates a more subtle problem. When deepfakes become commonplace, even authentic evidence can be dismissed as fabricated. Executives caught on video making inappropriate statements can claim the footage is AI-generated. Legitimate audio recordings can be contested. This plausible deniability cuts both ways, protecting the guilty while enabling doubt about the innocent.

Misinformation campaigns leverage generative AI to produce content at unprecedented scale. Fake news articles, fabricated research papers, synthetic social media profiles, all can be generated faster than human fact-checkers can respond. When employees can’t trust video calls, communications, or AI-generated recommendations, operational efficiency suffers.

For red teamers and security professionals, understanding these techniques enables building threat detection capabilities and training organizational awareness. The same methods used to create convincing fakes can inform the development of authentication systems and verification protocols. Teams building these skills should consider traditional Red Teaming foundations alongside AI-specific training.

AI systems integrated into enterprise workflows create new lateral movement pathways that traditional security tools struggle to detect. When AI becomes the attack vector itself, perimeter defenses offer limited protection.

IdentityMesh attacks, identified by researchers at Lasso Security, exploit a fundamental architectural vulnerability in agent AI systems. These agents typically operate with a unified operational identity that merges multiple authentication contexts, accessing CRMs, databases, email systems, and APIs under a single privileged account. Compromise that identity, and attackers gain access to every system the AI touches.

Indirect prompt injection through integrations represents an evolution of traditional injection attacks. Attackers embed malicious instructions in documents, emails, websites, or any content that AI agents process. When an AI assistant reads a compromised document, it may execute unauthorized actions, sending data to external servers, modifying records, or escalating privileges. The attack surface expands to include every information source the AI consumes.

MCP (Model Context Protocol) server vulnerabilities create additional attack paths. CSO Online’s 2025 research found numerous unprotected MCP servers exposing sensitive configurations and allowing unauthorized AI mode interactions. As these protocols standardize AI integrations, they also standardize potential entry points.

Excessive agency amplifies all other risks. When AI agents have broad permissions, write access to databases, the ability to send emails, and authority to execute code, attackers who manipulate those agents can take harmful actions at scale. An attacker submitting a malicious “contact us” inquiry could theoretically use a compromised AI agent to exfiltrate CRM data, send phishing emails to customers, and access internal databases, all through a single hijacked AI workflow.

This cross-system threat represents a paradigm shift. Traditional security focused on preventing unauthorized access. With adversarial AI techniques targeting these agents, the access is authorized; it’s the instructions that are compromised. This requires fundamentally different detection and prevention strategies.

Understanding attacker goals is the first step to building effective defenses. The techniques outlined above aren’t theoretical; they’re actively exploited in the wild, and their sophistication increases monthly.

Defensive principles must evolve accordingly. Zero-trust architecture extends to AI systems: verify inputs, limit agency, monitor outputs, and assume that any AI-processed content could contain malicious instructions. Regular LLM red teaming identifies vulnerabilities before attackers do. Input sanitization and output filtering provide defense in depth. Human-in-the-loop requirements for sensitive operations create checkpoints against autonomous compromise.

The skills gap is real. Traditional penetration testing expertise doesn’t fully transfer to AI systems. Prompt injection differs fundamentally from SQL injection. Adversarial machine learning requires an understanding of model architectures and training dynamics. Testing AI agents demands familiarity with their integration patterns and permission models.

OffSec’s LLM Red Teaming Learning Path addresses this gap with hands-on training in these exact techniques, supported by OWASP and MITRE frameworks that provide systematic methodologies for AI security assessment. For a comprehensive guide to building these capabilities, download Offensive Security in the Age of AI.

AI systems have become prime targets because they concentrate value: they hold sensitive data, influence critical decisions, enable trust exploitation at scale, and create lateral movement pathways through enterprise networks. Attackers recognize this, and they’re rapidly developing capabilities to exploit it.

The “think like an attacker” mindset that defines offensive security becomes even more critical in AI contexts. Security professionals must anticipate and simulate AI attacks, understanding data exfiltration techniques, model manipulation methods, trust erosion tactics, and lateral movement patterns, to build effective defenses.

As AI becomes more deeply integrated into critical systems, the security professionals who understand both sides of AI security will be essential. Organizations must also integrate threat intelligence on emerging AI attack vectors into their security programs. The question isn’t whether your AI systems will be targeted, but whether your team will be prepared when they are. The rise of sophisticated cyber attacks against AI infrastructure means that preparation today determines resilience tomorrow.

What is prompt injection and why is it dangerous?

Prompt injection is an attack technique where adversaries craft malicious inputs that manipulate AI systems into performing unintended actions. Attackers use prompt injection to bypass safety controls, extract confidential system instructions, leak sensitive training data, or execute unauthorized commands within integrated enterprise applications.

How do attackers poison AI training data?

Attackers poison AI training data by inserting malicious, biased, or manipulated content into datasets used to train machine learning models. Data poisoning enables adversaries to embed hidden backdoors, skew model outputs, or cause AI systems to produce incorrect predictions when triggered by specific inputs.

What are the signs an AI system has been compromised?

Signs that an AI system has been compromised include unexpected output patterns, responses containing sensitive information that should be restricted, unusual API usage spikes, degraded model accuracy, and AI agents performing actions outside their intended scope or authorization level.

How do deepfakes threaten enterprise security?

Deepfakes threaten enterprise security by enabling attackers to impersonate executives through synthetic audio and video for fraudulent wire transfers, credential theft, and social engineering attacks. Organizations face financial losses, reputational damage, and erosion of trust in legitimate communications.

What is LLMjacking and how can organizations prevent it?

LLMjacking occurs when attackers steal cloud credentials to gain unauthorized access to AI services, often costing victims thousands of dollars daily in compute charges. Organizations can prevent LLMjacking by implementing strong credential management, monitoring API usage for anomalies, and enforcing least-privilege access policies.

Why do traditional security tools struggle with AI threats?

Traditional security tools struggle with AI threats because they focus on perimeter defense and known attack signatures rather than semantic manipulation of model behavior. AI-specific vulnerabilities like prompt injection, adversarial inputs, and indirect manipulation require specialized detection methods that understand model context and intent.