The rapid deployment of generative AI systems across critical infrastructure has created an unprecedented security challenge: how do we effectively test and secure systems that can generate content, make decisions, and interact with users in ways we never fully anticipated — even with AI Red Teaming in place?
A groundbreaking study from Carnegie Mellon’s Software Engineering Institute reveals that while organizations rush to Red Team their AI systems, they’re missing decades of hard-won lessons from cybersecurity. Through systematic analysis of both fields, researchers uncovered critical gaps that could leave AI systems dangerously vulnerable – and actionable solutions that could transform how we approach AI security.
The Strategic Imperative: Current AI Red Teaming fixates on 85% jailbreaking attacks while Cyber Red Teaming evaluates 8+ distinct attack surfaces – your AI security posture is fundamentally incomplete.
[bctt tweet=”If your AI Red Team only tests jailbreaks, you’re securing the front door while leaving every window open. Real adversaries don’t care about your preferred attack vector. #AISecurityGap” username=”adversa_ai”]
The research reveals a critical blindspot in current AI security practices. While 93 out of 99 AI Red Teaming studies focused solely on direct prompt attacks (jailbreaking), Cyber Red Teaming comprehensively evaluates networks, applications, physical access, social engineering, wireless, IoT, mobile, and web applications.
AI systems interact with multiple attack surfaces beyond just prompts:
check Training data pipelines (only 1% of studies examined)
check RAG databases (5% coverage)
check Model deployment infrastructure (0% coverage)
check API endpoints and integration points (minimal coverage)
check Supply chain vulnerabilities (unexplored)
This narrow focus means organizations are essentially testing whether their AI can be tricked into saying bad things, while ignoring whether attackers could poison training data, compromise model weights, exfiltrate proprietary models, or attack the infrastructure hosting these systems.
— Step 1. Expand Your Threat Taxonomy.
— Step 2. Implement Multi-Surface Testing Framework.
— Step 3. Practical Multi-Vector Attack Simulation.
Example: Testing a customer service chatbot.
Traditional Approach: Try to make it say offensive things.
Comprehensive Approach:
check Poison FAQ database to inject malicious responses
check Attack API rate limiting to enable data extraction
check Social engineer access to admin panel
check Exploit logging systems to capture sensitive conversations
check Chain attacks. Use infrastructure access to modify system prompts
The Strategic Imperative: AI Red Teams skip pre-engagement planning and post-exploitation analysis, missing 40% of the value that Cyber Red Teams deliver through structured methodology.
[bctt tweet=”AI Red Teams are playing security theater – all attack, no strategy. Cyber taught us that 50% of security value comes from what happens before and after the hack. #RedTeamMaturity” username=”adversa_ai”]
The research identifies that Cyber Red Teaming follows 10 distinct operational stages, while AI Red Teaming typically engages in only 6, critically missing:
Missing Pre-Engagement Elements:
check Formal threat modeling with stakeholders
check Rules of engagement definition
check Legal framework establishment
check Success criteria alignment
Missing Post-Exploitation Elements:
check Structured vulnerability prioritization
check Mitigation effectiveness testing
check Knowledge transfer protocols
check Continuous improvement frameworks
Only 8 out of 99 AI Red Teaming papers reported responsible disclosure, and none discussed formal engagement planning. This means AI security tests often lack clear objectives, miss critical vulnerabilities due to poor scoping, and fail to translate findings into actionable improvements.
— Phase 1. Pre-Engagement Protocol Implementation.
— Phase 2. Structured Execution Framework.
— Phase 3. Post-Engagement Value Delivery.
Real-World Example Implementation.
Before: “We tested GPT-4 and found 73% jailbreak success rate”
After:
check Pre-engagement: 2-day threat modeling with 6 stakeholders
check Defined 4 adversary profiles, 12 loss scenarios
check Tested 6 attack surfaces, 23 attack chains
check Findings: 3 Critical, 7 High, 15 Medium vulnerabilities
check Delivered: 15 detection rules, 8 architectural fixes, 3 process improvements
check 90-day remediation tracking with bi-weekly validation tests
The Strategic Imperative: While Cyber Red Teams leverage 400+ mature open-source tools with 86% availability, AI Red Teams rely on ad-hoc scripts and manual prompt crafting, limiting scope and repeatability.
[bctt tweet=”Cyber Red Teams have Metasploit. AI Red Teams have Python scripts and prayer. Guess which one scales to enterprise security? #AISecurityTools” username=”adversa_ai”]
The research reveals a massive tooling gap: cyber security benefits from decades of tool development with specialized solutions for every attack stage:
check Reconnaissance: Nmap, Shodan, Recon-ng.
check Vulnerability Scanning: Nessus, Burp Suite, OWASP ZAP.
check Exploitation: Metasploit, Cobalt Strike, Empire.
check Post-exploitation: Mimikatz, BloodHound, PowerSploit.
In contrast, AI Red Teaming tools are:
This means every AI security assessment starts from scratch, can’t leverage community knowledge, and misses vulnerabilities that automated scanning would catch.
Building an AI Security Testing Arsenal:
— Tool Category 1. Automated Vulnerability Scanners.
— Tool Category 2. Exploit Framework.
— Tool Category 3. Infrastructure Testing Suite.
The convergence of cyber and AI security isn’t just an academic exercise – it’s an operational imperative. As this research demonstrates, AI Red Teaming is currently operating at the maturity level of cyber security circa 2005: enthusiastic but ad-hoc, narrowly focused, and lacking the systematic approaches that took the cybersecurity industry decades to develop.
The three insights presented here – expanding from narrow jailbreak testing to comprehensive threat modeling, implementing structured engagement methodologies, and building industrial-scale tooling – represent more than incremental improvements. They’re transformative shifts that can mean the difference between security theater and actual AI system resilience.
Organizations that act on these insights now won’t just be more secure; they’ll be building the playbooks, tools, and expertise that will define AI security excellence for the next decade. The question isn’t whether to adopt these cyber-learned practices for AI security – it’s whether you’ll be among the leaders who shape this evolution or the followers trying to catch up after the first major AI security incident makes headlines.
The time for amateur hour in AI security is over. The lessons are there, hard-won through decades of cyber battles. All that remains is the will to apply them.
Explore the Top GenAI Resources to stay informed about the most pressing risks and defenses in the field. As GenAI becomes deeply integrated into products, workflows, and user-facing systems, attackers ...
Introducing Universal LLM Jailbreak approach. Subscribe for the latest AI Jailbreaks, Attacks and Vulnerabilities If you want more news and valuable insights on a weekly and even daily basis, follow [...]
