Every major security breakthrough begins the same way: attackers find a new surface before defenders think to look there. It happened with networks, web applications, and the cloud. Now, as organizations embed artificial intelligence into their most critical operations, it is happening with AI systems as well, and conventional security testing was never designed to address it. That gap is exactly what AI red teaming techniques were built to close.
“AI red teaming reveals how attackers interact with AI systems in practice, exposing risks that traditional security testing often misses.” – IOActive Security Team
AI Red Teaming Techniques
Prompt Injection
What it is
A class of attacks where adversaries insert malicious instructions into AI input channels to override system behavior, hijack outputs, or trigger unauthorized actions.
How it works
Direct attacks manipulate user-facing prompts to override system instructions. Indirect attacks embed malicious commands inside content the AI trusts: retrieved documents, browsed webpages, or received emails. This allows attackers to compromise a system without ever interacting with it directly. Red teams use tools such as Garak and PromptBench to generate and score injection payloads across both vectors systematically. A structured engagement first maps every external input source the model trusts, then tests each path with escalating payload complexity to identify where instructions can be overridden.
Expected outcomes and benefits
Testing exposes which input paths, data sources, and integration points carry the highest risk of compromise. Organizations gain a clear map of where instruction override is possible and under what conditions.
Limitations
Injected instructions are difficult to detect because they can closely mimic legitimate input. Full coverage requires testing both direct and indirect vectors, and indirect paths multiply as AI systems access more external data sources.
Best for
Organizations deploying LLMs with access to external data, retrieval-augmented generation pipelines, agentic workflows, or enterprise integrations such as email, document management, and third-party APIs.
Prompt injection is the defining vulnerability class of the AI era. In direct attacks, adversaries manipulate user input to override system instructions. In indirect attacks, malicious instructions are embedded inside content the AI trusts: retrieved documents, browsed webpages, or received emails.
The risk scales with access. AI agents connected to enterprise platforms move 16 times more data than human users, turning a single compromised agent into an organization-wide exposure event, according to Obsidian Security. Indirect attacks now account for over 55% of observed prompt injection incidents, with 20 to 30% higher success rates than direct attacks due to stealth delivery through trusted content. OWASP ranks prompt injection as the number one vulnerability in its 2025 Top 10 for LLM Applications, with 73% of audited production systems showing exposure.
Jailbreak Testing
What it is
Systematic attempts to bypass the safety mechanisms, content filters, and alignment controls built into AI models, exposing gaps between intended behavior and actual behavior under adversarial pressure.
How it works
Red teams probe model guardrails using roleplay scenarios, multi-turn escalation sequences, adversarial rephrasing, cross-language attacks, and varied stylistic prompts designed to shift model behavior beyond its trained constraints.
Tools such as PyRIT (Microsoft’s Python Risk Identification Toolkit for red teamers) automate payload generation and track bypass rates across test runs, distinguishing a structured red team evaluation from ad hoc user experimentation. A formal engagement establishes a behavioral baseline, tests attack categories systematically, and maps each bypass to the specific guardrail or alignment control that failed.
Expected outcomes and benefits
Testing identifies which safety controls hold under pressure and which fail, along with the specific attack patterns most likely to succeed against a given deployment. Organizations can prioritize remediation based on realistic risk, not theoretical exposure.
Limitations
Success rates vary by model version and configuration. Not all bypasses carry equivalent risk, and some remediation options depend on controls that model providers, not operators, must implement.
Best for
Organizations deploying customer-facing LLMs, models with access to sensitive or regulated data, or AI systems where harmful outputs carry legal, reputational, or operational consequences.
Jailbreaks target safety mechanisms, bypassing the content filters and alignment controls built into a model. Roleplay-based attacks achieve 89.6% success rates against large language models. Multi-turn sequences, where an attacker escalates gradually across a conversation, reach 97% success within five exchanges. AI red teams probe these boundaries systematically across diverse attack styles, languages, and escalation strategies.
AI Agent Abuse and Privilege Escalation
What it is
Testing that targets autonomous AI agents’ susceptibility to goal hijacking, tool misuse, and unauthorized privilege escalation across the APIs, databases, and multi-agent environments they operate in.
How it works
Red teams attempt to redirect agent objectives, abuse tool access, and escalate privileges across agent architectures. In multi-agent environments, testers evaluate whether a single injected instruction can propagate across co-running agents in a cascading compromise.
Engagements begin by mapping the agent’s full tool inventory, permission scope, and trust relationships before testing whether crafted inputs can redirect agent behavior toward attacker-controlled objectives. MITRE ATLAS and custom agentic attack playbooks structure the engagement by threat actor objective rather than by system component.
Expected outcomes and benefits
Testing uncovers exploitable flaws in agent tool-execution logic, quantifies cascading risk in multi-agent pipelines, and validates whether permission boundaries hold under adversarial conditions.
Limitations
Scope grows significantly in complex multi-agent deployments. Comprehensive coverage requires deep access to agent architecture, tool configurations, and the external systems agents are authorized to reach.
Best for
Organizations running agentic AI workflows connected to enterprise systems, databases, or APIs, particularly where agents can take autonomous action such as sending emails, executing transactions, or modifying records.
Agentic AI introduces risks with no parallel in traditional testing. Agents plan, take action, and use tools: sending emails, querying databases, calling APIs, and chaining multi-step operations autonomously.
40% of AI agent frameworks contain exploitable prompt injection flaws in their tool-execution logic. Autonomous agents that call external APIs carry up to 2.5 times higher risk exposure than standalone models. In multi-agent environments, a single injected instruction can propagate to 48% of co-running agents in a cascading compromise. Red teams test for goal hijacking, tool misuse, and privilege escalation across these architectures.
Training Data Attacks and RAG Poisoning
What it is
Attacks that manipulate the data an AI system retrieves or learns from, corrupting its outputs and steering its behavior without touching the model itself.
How it works
Adversaries introduce crafted documents into retrieval pipelines or training datasets. Even a small number of poisoned entries can skew AI responses at scale. An adversary who controls what the AI retrieves effectively controls what it recommends and what actions it takes.
Red teams inject crafted documents into the retrieval corpus and query the system iteratively to measure how reliably poisoned content influences model outputs. Testing also covers chunk-level embedding manipulation and retrieval ranking abuse to determine how little adversarial content is required to achieve meaningful response distortion.
Expected outcomes and benefits
Testing reveals vulnerabilities in data ingestion pipelines, exposes how content control translates to output manipulation, and identifies supply chain risk in AI systems that rely on external or user-contributed data.
Limitations
Poisoning effects can be subtle and difficult to detect without targeted testing. Comprehensive assessment requires visibility into retrieval pipelines, document ingestion processes, and embedding logic, which may require close coordination with internal data and engineering teams.
Best for
Organizations using RAG architectures, enterprise knowledge bases, or AI systems trained on internal or third-party data, particularly where retrieved content directly shapes model output or user-facing recommendations.
Retrieval-Augmented Generation systems, which ground model responses in dynamically retrieved content, are vulnerable to data pipeline manipulation. Research published in Information (2026) shows that just five carefully crafted documents can manipulate AI responses 90% of the time through RAG poisoning. As IOActive has documented in its analysis of downstream AI attacks, an adversary who controls what an AI retrieves can control what it says and what it does.
Model Manipulation and Multimodal Attacks
What it is
Attacks targeting model confidentiality and cross-modal vulnerabilities, including efforts to reconstruct proprietary model behavior or embed malicious instructions inside image and audio inputs.
How it works
Model extraction attacks use crafted query sequences to map and replicate model behavior, enabling intellectual property theft or the creation of a shadow model for further exploitation. Multimodal attacks embed adversarial instructions inside images or audio accompanying benign requests, triggering unauthorized actions across input channels.
Extraction testing uses systematic query strategies to probe model decision boundaries and reconstruct behavior patterns without direct model access. Multimodal testing employs tools such as Burp Suite AI extensions and adversarial image generation frameworks to embed instructions inside image or audio payloads, then validates whether the target system processes them as executable commands.
Expected outcomes and benefits
Testing identifies intellectual property exposure, reveals how non-text inputs expand the attack surface, and validates whether input sanitization holds across all supported modalities.
Limitations
Model extraction requires significant query volume to produce a useful reconstruction. Multimodal testing scope depends on which input types the deployment supports, and organizations with restricted API access may face constraints on extraction simulations.
Best for
Organizations with proprietary or fine-tuned models, multimodal AI deployments, or any system that accepts user-submitted images, audio, or document files as part of its core workflow.
Model extraction attacks use crafted queries to reconstruct proprietary model behavior, enabling intellectual property theft or the development of a shadow model for further attacks. As AI systems expand to process images and audio alongside text, adversaries can embed malicious instructions inside images that accompany benign content, triggering unauthorized actions across input channels, according to OWASP’s LLM01:2025 guidance.
The techniques above represent the current front lines of AI adversarial testing. The urgency to apply them is driven by a threat environment that is moving faster than most organizations have adapted.
Why These Threats Are Escalating
The most consequential shift in the AI threat landscape is the rise of autonomous, tool-using agents operating across enterprise environments. A critical vulnerability in Microsoft 365 Copilot (CVE-2025-32711), rated CVSS 9.3, demonstrated how AI command injection in a widely deployed enterprise agent could enable large-scale data theft, as reported by Trend Micro. Controlled security experiments recorded credential theft or system manipulation outcomes in 70% of agent-based prompt injection trials.
OWASP’s December 2025 Top 10 for Agentic Applications and MITRE ATLAS’s October 2025 update, adding 14 new AI-focused techniques, reflect how quickly this space is evolving. The EU AI Act requires adversarial testing for high-risk AI systems ahead of its August 2026 compliance deadline, adding regulatory weight to what was already a security imperative.
The regulatory deadlines and real-world incidents above are not edge cases. They are the baseline risk environment organizations are now operating in, and the financial case for action reflects that reality.
The Business Case for AI Red Teaming
The business case for investing in AI red teaming is grounded in real incidents. 77% of businesses have reported an AI-related security incident. 35% of those incidents were caused by simple prompts, with some resulting in losses exceeding $100,000 per event, according to Adversa AI’s 2025 Security Report via Vectra AI. Organizations that run mature AI red teaming programs report 60% fewer AI-related security incidents than those without.
AI Red Teaming By The Numbers
Organizations are responding by increasingly investing in AI red teaming services. The market was valued at $2.26 billion in 2026 and is projected to reach $6.17 billion by 2030, growing at a 28.5% compound annual growth rate, according to Research and Markets. Demand for AI red teaming is forecasted to surge 35% by 2028, with qualified practitioners already in short supply, according to Practical DevSecOps.
The organizations best positioned to capture that advantage are those that can move beyond generic assessments and apply AI red teaming techniques grounded in real adversary behavior.
The IOActive Approach
IOActive brings decades of adversary simulation expertise to the full spectrum of AI red teaming techniques. The offensive instincts that break into financial institutions and critical infrastructure apply directly to AI systems that reason, decide, and act. IOActive connects proven red team methodology to the emerging AI attack taxonomy, testing how real adversaries would engage with an organization’s models, agents, and data pipelines.
A full engagement covers threat modeling scoped to the organization’s specific AI architecture, prompt injection testing across direct and indirect paths, jailbreak and safety bypass evaluation, agent workflow abuse testing, RAG and training data integrity assessment, privilege escalation validation, and multimodal attack surface review.
The output is a realistic picture of exploitable risk, produced under controlled conditions, with time to remediate before a real adversary finds the same vulnerabilities in production.
The vulnerabilities in your AI systems exist whether or not you have tested for them.
IOActive helps organizations find and fix exploitable risks in large language models, AI agents, and generative systems before adversaries do.