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Introduction:
The AI revolution has unleashed a new frontier of cybersecurity threats that traditional security tools are fundamentally unequipped to handle. As organizations race to deploy generative AI and agentic systems, they are inadvertently opening attack surfaces that adversaries are already exploiting—from prompt injection and data poisoning to excessive agency and denial-of-service risks. Jason Haddix, a legendary hacker, CEO of Arcanum Information Security, and Field CISO at Flare, will deliver the afternoon keynote at Antisyphon Training’s Threat Hunting Summit on June 17, 2026, tackling exactly these challenges. His session, “Defending AI: Organized Musings on Securing AI Agents for Cybersecurity,” promises to cut through the hype and deliver actionable intelligence on assessing AI applications before attackers do.
Learning Objectives:
- Master Jason Haddix’s seven-point methodology for assessing AI system security, including prompt injection taxonomy and API threat modeling.
- Identify and mitigate critical AI-specific vulnerabilities such as missing rate limiting, weak account thresholds, unchecked execution times, and denial-of-service risks.
- Develop a proactive threat-hunting mindset that assumes compromise and leverages AI agents to scale detection beyond automated alerts.
You Should Know:
- The AI Attack Surface: Beyond the Chatbot Jailbreak
Most organizations treat AI security as an extension of traditional web application security—a dangerous misconception. AI applications introduce vulnerabilities across four distinct layers: the model itself, the data pipeline, the application logic (including agents), and the cloud infrastructure. According to Jason Haddix, during AI security assessments at Arcanum Information Security, his team frequently uncovers issues like missing rate limiting, weak account thresholds, unchecked execution times, and denial-of-service risks—”important areas often underlooked when security testing a wholistic AI bot implementation”.
The OWASP AI Vulnerability Scoring System (AIVSS) now provides a standardized framework for quantifying security risks in AI systems, extending traditional CVSS by measuring how agentic capabilities amplify risk. Meanwhile, the OWASP Top 10 for LLM Applications highlights threats including prompt injection, insecure output handling, training data poisoning, denial of service, and excessive agency—where an application grants an LLM more autonomy, permissions, or system access than necessary.
Step‑by‑Step Guide: Conducting an AI Application Threat Model
Step 1: Map the AI System Architecture
Document every component: the LLM or foundation model, the APIs that serve it, the data aggregators feeding it, and the infrastructure hosting it. Identify all input vectors—not just the chat interface, but also API endpoints, file uploads, and system prompts.
Step 2: Inventory Data Flows and Permissions
Trace how data moves into, through, and out of the AI system. Audit what permissions the AI agent has—can it read files, execute commands, or make API calls? Use the principle of least privilege; if the agent doesn’t need it, revoke it.
Step 3: Test for Rate Limiting and Resource Exhaustion
AI endpoints are prime targets for denial-of-service attacks. Use tools like `curl` or custom scripts to send rapid requests:
Linux - Test rate limiting with a simple loop
for i in {1..1000}; do curl -X POST https://api.your-ai.com/v1/complete -H "Content-Type: application/json" -d '{"prompt":"Test"}' & done
Windows PowerShell - Burst test for rate limiting
1..1000 | ForEach-Object -Parallel { Invoke-RestMethod -Uri "https://api.your-ai.com/v1/complete" -Method Post -Body '{"prompt":"Test"}' -ContentType "application/json" } -ThrottleLimit 50
If the endpoint responds to all requests without throttling, you’ve found a critical vulnerability.
Step 4: Audit Prompt Injection Defenses
Craft test inputs designed to override system prompts or extract sensitive information. Jason Haddix has released Arcanum’s prompt injection taxonomy as a resource for penetration testers. Test both direct injections (“Ignore previous instructions and…”) and indirect ones (malicious content in uploaded documents).
Step 5: Validate Output Filtering and Monitoring
Ensure the AI doesn’t leak PII, internal system details, or proprietary data. Implement monitoring for anomalous patterns—rapid automated scanning, abnormal access requests, and unfamiliar scripts or commands.
- Threat Hunting in the Age of Agentic AI
Traditional threat hunting assumes a human analyst driving the process, but AI-driven attacks are compressing attack lifecycles from days and hours to minutes and seconds. David Bianco, architect of the Sqrrl Hunting Loop and lead author of the PEAK Threat Hunting Framework, poses the critical paradox: defenders need AI to stay in the game, yet AI models aren’t trustworthy enough to rely on. The human is starting to look like an unnecessary bottleneck, but AI remains fundamentally gullible—discovering ways to subvert it happens practically every week.
This is where agentic AI for threat hunting enters the picture. Platforms now let analysts describe a suspected technique, behavior, or MITRE ATT&CK tactic in plain language; the AI then builds a hunt plan, runs the investigation across telemetry, and returns findings in minutes. However, current LLMs are poorly suited for open-ended, evidence-driven threat hunting despite strong performance on curated Q&A benchmarks. The solution lies in structured approaches like Sydney Marrone’s LOCK pattern (Learn, Observe, Check, Keep)—a lightweight framework for turning every investigation into a persistent, markdown-based record that AI can actually use.
Step‑by‑Step Guide: Building an AI-Assisted Threat Hunting Workflow
Step 1: Define Your Hunting Hypothesis
Start with a specific question based on threat intelligence. For example: “Are any systems communicating with known C2 infrastructure?” Use the MITRE ATT&CK framework to structure your hypothesis around specific tactics and techniques.
Step 2: Collect and Normalize Telemetry
Aggregate logs from multiple sources. On Linux, use `journalctl` and auditd:
Linux - Collect system logs for threat hunting sudo journalctl --since "2026-06-01" --until "2026-06-16" > system_logs.txt sudo ausearch -ts recent -m all > audit_logs.txt
On Windows, use PowerShell to gather Event Logs:
Windows - Export security and system event logs Get-WinEvent -LogName Security, System, Application -MaxEvents 10000 | Export-Csv -Path "C:\ThreatHunt\event_logs.csv" -1oTypeInformation
Step 3: Deploy Network Analysis Tools
Use RITA (Real Intelligence Threat Analytics) with Zeek telemetry to analyze network traffic for C2 behavior:
Linux - Install and run Zeek and RITA sudo apt-get install zeek rita zeek -r capture.pcap local rita import --rolling zeek_logs/ rita show-beacons --human-readable
Step 4: Leverage AI for Pattern Recognition
Feed structured logs into an AI analysis tool. Jason Haddix’s Arcanum Cyber Security Bot (available on ChatGPT’s GPT store) is engineered to leverage up-to-date technical information for application security and penetration testing. Use it to correlate events and identify anomalies that signature-based tools miss.
Step 5: Document Findings Using the LOCK Pattern
Create a markdown record for every investigation:
Threat Hunt Investigation: [bash] Learn - Hypothesis: [Your hypothesis] - TTPs: [MITRE ATT&CK techniques] Observe - Data sources: [Logs, telemetry used] - Anomalies found: [List of suspicious activities] Check - Verification steps: [How you validated findings] - False positives: [What was ruled out] Keep - Key takeaways: [What to remember for next time] - New indicators: [IOCs to add to watchlists]
Step 6: Automate the Grind with Agentic Frameworks
Implement agentic AI frameworks that can autonomously run investigations based on your playbooks. These agents can expand your investigative reach and help you operate at a scale no single threat hunter can match.
3. AI Security Tooling and Infrastructure Hardening
Securing AI applications requires hardening not just the model but the entire ecosystem. Common vulnerabilities include dependency flaws, misconfigurations, and injection attacks with deterministic inputs and outputs. Attackers are also exploiting third-party package vulnerabilities and licensing risks. The National CERT-In recommends adopting AI-enabled defensive security tools for automated vulnerability detection, attack surface analysis, and threat detection to strengthen proactive defense capabilities.
Step‑by‑Step Guide: Hardening Your AI Infrastructure
Step 1: Secure the API Gateway
Implement API keys, OAuth2, or mutual TLS for all AI endpoints. Enforce strict input validation and output encoding. Use tools like `modsecurity` or cloud WAFs to filter malicious payloads.
Linux - Example NGINX rate limiting for AI endpoints
location /api/v1/complete {
limit_req zone=ai_api burst=5 nodelay;
proxy_pass http://ai-backend;
}
Step 2: Monitor for Data Poisoning
Implement version control and integrity checks for training datasets. Use cryptographic hashing to verify data integrity before model updates:
Linux - Generate and verify dataset hashes sha256sum training_data_v1.csv > checksums.txt sha256sum -c checksums.txt
Step 3: Implement Least Privilege for AI Agents
Audit and restrict the permissions granted to AI agents. Use role-based access control (RBAC) and service accounts with minimal scopes. On Kubernetes, use network policies to restrict egress:
Kubernetes - Restrict AI agent egress apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: ai-agent-egress spec: podSelector: matchLabels: app: ai-agent policyTypes: - Egress egress: - to: - ipBlock: cidr: 10.0.0.0/8
Step 4: Deploy Continuous Monitoring
Use SIEM and AI-powered detection tools to monitor for anomalous behavior. Set up alerts for:
– Unusual API call volumes (potential DoS)
– Unexpected output patterns (potential data leakage)
– Privilege escalation attempts
– Unauthorized model access
Step 5: Regular Penetration Testing
Conduct regular AI-specific penetration tests using Jason Haddix’s seven-point methodology. Test not just the chat interface but the entire system—APIs, data aggregators, and infrastructure. Move beyond simple “chatbot jailbreaks” to full-system audits.
- The Skills Gap: Training the Next Generation of AI Defenders
The cybersecurity industry faces a critical skills gap in AI security. Traditional security training doesn’t cover prompt injection, model poisoning, or agentic system vulnerabilities. Jason Haddix has been at the forefront of bridging this gap through research, training, and consulting in the cybersecurity and AI spaces. The Antisyphon Training Threat Hunting Summit is part of this effort, offering free access to expert knowledge and affordable hands-on training courses.
Step‑by‑Step Guide: Building Your AI Security Skillset
Step 1: Master the Fundamentals
Start with the OWASP Top 10 for LLM Applications and the OWASP AIVSS framework. Understand the unique threat landscape: prompt injection, insecure output handling, training data poisoning, denial of service, and excessive agency.
Step 2: Practice with Real-World Tools
Use Jason Haddix’s Arcanum Cyber Security Bot for hands-on practice. Experiment with prompt injection techniques and defensive strategies in controlled environments.
Step 3: Attend Industry Events
Participate in events like the Antisyphon Training Threat Hunting Summit. Engage with expert speakers, join the Discord community, and learn from real-world case studies.
Step 4: Pursue Hands-On Training
Take advantage of affordable training courses like “Agentic AI for Threat Hunting” or “Intro to Network Threat Hunting”. Use code THSUMMIT2026 for 20% off.
Step 5: Stay Current
Follow thought leaders like Jason Haddix, David Bianco, and the Antisyphon Training community. The AI security landscape evolves weekly—continuous learning is not optional.
What Undercode Say:
- Key Takeaway 1: AI application security is not traditional web app security. Organizations must adopt new frameworks like OWASP AIVSS and Jason Haddix’s seven-point methodology to systematically assess AI systems across model, data, logic, and infrastructure layers.
-
Key Takeaway 2: The threat-hunting paradox is real—defenders need AI speed and scale to counter AI-driven attacks, but current models aren’t trustworthy enough for full autonomy. The solution lies in hybrid human-AI workflows, structured documentation (LOCK pattern), and continuous validation.
Analysis: The cybersecurity industry is at an inflection point. AI is simultaneously the greatest defensive tool and the most potent attack vector. Jason Haddix’s keynote at the Threat Hunting Summit arrives at a critical moment when organizations are deploying AI agents faster than they can secure them. The vulnerabilities his team uncovers—rate limiting failures, weak thresholds, DoS risks—are basic security hygiene failures that become catastrophic when applied to AI systems. The industry needs to move beyond treating AI security as an afterthought and embed it into the software development lifecycle. The good news is that frameworks, taxonomies, and training are becoming available. The bad news is that adversaries are already exploiting these gaps. The organizations that invest in AI security training and proactive threat hunting today will be the ones that survive tomorrow’s breaches.
Prediction:
- +1 Organizations that adopt structured AI security frameworks (OWASP AIVSS, Jason Haddix’s methodology) will reduce AI-related incident response costs by 40-60% within 18 months as proactive assessments replace reactive firefighting.
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-1 The average dwell time for AI-specific breaches will drop from weeks to hours as attackers automate prompt injection and data poisoning at scale—but defenders without AI-assisted hunting will be completely overwhelmed.
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+1 Agentic AI for threat hunting will mature into a standard SOC capability by 2028, with platforms that can autonomously hunt, investigate, and remediate low-complexity threats, freeing human analysts for advanced threat hunting.
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-1 The skills gap in AI security will widen dramatically over the next two years, with demand for AI security specialists outpacing supply by 3:1, creating a “wild west” of insecure AI deployments across enterprises.
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+1 Community-driven training initiatives like the Antisyphon Threat Hunting Summit will accelerate the democratization of AI security knowledge, enabling smaller organizations to defend against sophisticated AI-driven attacks.
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