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Introduction:
The cybersecurity industry has long operated on a reactive model—waiting for alerts, chasing false positives, and responding after breaches have already occurred. But as attackers grow more sophisticated and automated, defenders must evolve. Threat hunting represents a paradigm shift: assuming compromise, searching beyond traditional alerts, and uncovering adversaries hiding in plain sight. At the forefront of this evolution is Jason Haddix—hacker, CEO, and CISO—whose cutting-edge research in AI security and threat intelligence is reshaping how modern security teams operate. The Antisyphon Training Threat Hunting Summit, held on June 17, 2026, brought together thousands of security practitioners to explore practical methodologies for detecting stealthy adversaries, investigating suspicious behavior, and turning discoveries into actionable defenses. This article distills the summit’s key technical insights, from AI-driven hunting to memory forensics and LOLBIN abuse, into a comprehensive guide for security professionals ready to move from reactive to proactive defense.
Learning Objectives:
- Understand the core principles of proactive threat hunting and how to assume compromise in your environment.
- Master practical techniques for hunting Living Off the Land (LOLBIN) binaries on Windows and persistence mechanisms on Linux.
- Learn how AI agents and large language models can augment threat hunting workflows, from hypothesis generation to automated investigation.
- Develop hands-on skills with open-source tools like Osquery, RITA, and Wazuh for behavioral analysis and anomaly detection.
You Should Know:
- The Hunter’s Mindset: Assuming Compromise and Proactive Defense
Traditional security operations center (SOC) workflows are alert-driven: a tool generates a notification, an analyst investigates, and a ticket is closed. Threat hunting inverts this model. Instead of waiting for alerts, hunters proactively search for indicators of compromise (IoCs) and behaviors that deviate from normal baselines. As David Bianco’s keynote at the summit framed it, this is “The Hunter’s Paradox”—the tension between automated detection and human intuition. The core premise is simple: assume that attackers are already inside your network. This mindset shifts the defender’s role from reactive firefighter to proactive investigator.
To operationalize this, security teams should adopt hypothesis-driven hunting. Start with a question: “How would an attacker move laterally in my environment?” or “What would credential dumping look like in my logs?” Then, use data sources—endpoint logs, network flows, and authentication records—to test that hypothesis. Tools like the PEAK Threat Hunting Assistant, an agentic AI system developed by Cisco, can accelerate this process by automating research and planning for hypothesis-driven hunts. The goal is not to find every threat but to reduce the mean time to detection (MTTD) by surfacing subtle anomalies that automated rules miss.
- Hunting LOLBINS: Detecting Living Off the Land Attacks on Windows
One of the most challenging threats to detect is the use of LOLBINs (Living Off the Land Binaries)—legitimate Windows executables that attackers abuse to evade security controls. As highlighted in the summit’s training track “LOLBINS vs. LOLBINS: Endpoint Threat Hunting,” adversaries frequently leverage tools like rundll32.exe, certutil.exe, and `wmic.exe` to execute malicious code, download payloads, or dump credentials without triggering traditional antivirus alerts.
Step‑by‑step guide to hunting LOLBIN abuse:
Step 1: Establish a baseline.
Collect a week’s worth of process creation logs (via Sysmon or Windows Event Logging) and identify normal usage patterns for common LOLBINs. For example, `rundll32.exe` is often invoked by legitimate software updates; learn what that looks like in your environment.
Step 2: Hunt for suspicious command-line arguments.
Use PowerShell to query recent events for LOLBINs with unusual parameters. For rundll32.exe, look for execution of `.jpg` or `.png` files, which may indicate side-loading attacks.
Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} | Where-Object { $<em>.Message -match 'rundll32.exe' -and $</em>.Message -match '.jpg|.png' }
Step 3: Monitor for encoded commands.
Attackers often use `certutil.exe` with the `-decode` parameter to base64-decode and execute malicious scripts. Hunt for command lines containing `-decode` or -urlcache:
Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} | Where-Object { $<em>.Message -match 'certutil.exe' -and ($</em>.Message -match '-decode' -or $_.Message -match '-urlcache') }
Step 4: Correlate with network logs.
If a LOLBIN makes an outbound connection to an unknown IP, that’s a strong indicator of compromise. Use tools like RITA (Real Intelligence Threat Analytics) to analyze Zeek logs for Beaconing behavior—a talk by Hermon Kidane at the summit covered this exact technique.
Step 5: Automate detection.
Deploy a custom PowerShell script that scans running processes every 5 minutes for suspicious LOLBIN activity. Alert on any instance of `rundll32.exe` or `regsvr32.exe` executing from non-standard directories (e.g., %TEMP%).
3. Linux Persistence Hunting: Techniques Security Teams Miss
While Windows LOLBINs are well-documented, Linux environments present their own hunting challenges. Attackers frequently establish persistence through cron jobs, systemd timers, and shell configuration files. A recent study on “Linux Persistence Hunting” identified five techniques that security teams most often overlook.
Step‑by‑step guide to hunting Linux persistence:
Step 1: Audit cron and systemd timers.
Check for @reboot entries in crontab, which execute commands every time the system starts—a favorite for malware reinfection.
for user in $(cut -f1 -d: /etc/passwd); do crontab -u $user -l 2>/dev/null | grep '@reboot'; done
Step 2: Examine shell startup files.
Injecting commands into `.bashrc` or `.profile` allows attackers to run malicious code every time a user opens a terminal.
find /home -1ame ".rc" -exec grep -l "curl|wget|nc|bash -i" {} \;
Step 3: Use Osquery for real‑time monitoring.
Osquery converts the operating system into a high-performance relational database. Query for unexpected processes or file modifications:
SELECT FROM processes WHERE name IN ('bash', 'sh', 'python', 'perl') AND parent NOT IN ('init', 'systemd');
Step 4: Deploy Sysmon for Linux.
Microsoft’s Sysmon for Linux provides detailed process and network event logging, which can be fed into a SIEM for correlation.
Step 5: Leverage PersistHunt.
The open-source tool `persisthunt` automates persistence detection and artifact collection, categorizing findings into High, Low, and Informational severity levels.
git clone https://github.com/raj3shp/persisthunt.git cd persisthunt python3 persisthunt.py -d /path/to/collect
- Agentic AI for Threat Hunting: From Hypothesis to Investigation
Perhaps the most transformative topic at the summit was the integration of AI agents into threat hunting workflows. Faan Rossouw’s talk, “How AI Agents Solve Threat Hunting’s Biggest Problem,” and Jason Haddix’s keynote, “Defending AI: Organized Musings on Securing AI Agents for Cybersecurity,” both emphasized that AI is not just a buzzword but a practical force multiplier.
Agentic AI systems go beyond simple large language model (LLM) calls; they consist of teams of cooperating agents capable of goal-directed reasoning, tool use, and automated feedback loops. For threat hunters, this means AI can now generate hypotheses, write KQL or Sigma queries, interpret results, and even suggest next steps—all in natural language.
Step‑by‑step guide to integrating AI into your hunt:
Step 1: Deploy an on‑premise AI layer.
Use open-source models with LM Studio or Ollama to keep sensitive data internal. Project Raven, for example, combines a multi-provider AI layer with ML anomaly detection and automated kill-chain planning.
Step 2: Build a hypothesis generation pipeline.
Feed your AI agent with threat intelligence feeds and recent CVE data. Ask it: “Based on the latest APT29 TTPs, what hunting queries should I run today?” The agent should return a list of prioritized hypotheses.
Step 3: Automate query execution.
Integrate the AI agent with your SIEM or endpoint detection and response (EDR) platform. Microsoft’s Security Copilot Threat Hunting Agent can generate and run KQL queries directly in Defender XDR.
Step 4: Use AI for memory forensics.
Jamie Levy’s talk, “Memory Forensics for Everyone,” demonstrated how AI can simplify complex memory analysis. Tools like Volatility 3 can be augmented with AI to automatically identify malicious processes, hidden kernels, and injected code.
Step 5: Secure the AI itself.
As Haddix stressed, securing AI agents is critical. Attackers are now targeting the entire LLM ecosystem—not just prompt injection but also data aggregation pipelines and infrastructure. Implement strict input validation, monitor API calls for anomalies, and treat AI agents as privileged assets.
- Network Threat Hunting: Behavioral Analysis of C2 Traffic
Network logs remain one of the richest data sources for threat hunters. However, the sheer volume of traffic makes manual analysis impractical. Behavioral analysis tools like RITA (Real Intelligence Threat Analytics) can automatically flag command-and-control (C2) traffic based on Beaconing patterns, DNS tunneling, and JA3/S hashes.
Step‑by‑step guide to hunting C2 with RITA:
Step 1: Collect Zeek logs.
Deploy Zeek (formerly Bro) on your network tap or SPAN port to generate rich connection logs (conn.log), DNS logs (dns.log), and HTTP logs (http.log).
Step 2: Install and configure RITA.
RITA is open-source and designed to ingest Zeek logs. Install it on a dedicated analysis server:
git clone https://github.com/activecm/rita.git cd rita make sudo make install
Step 3: Import and analyze data.
Point RITA at your Zeek log directory and let it run behavioral analysis:
rita import --datadir /path/to/zeek/logs /path/to/rita/database rita show-beacons /path/to/rita/database
Step 4: Investigate flagged beacons.
RITA will output a list of IP addresses and domains exhibiting Beaconing behavior—periodic, outbound connections that are highly indicative of C2. Cross-reference these with threat intelligence feeds.
Step 5: Automate with AI.
Integrate RITA’s output with an AI agent that can automatically enrich flagged IPs with geolocation, WHOIS, and reputation data, then escalate high-confidence findings to the SOC.
6. Memory Forensics for Everyone: Simplifying Volatility 3
Memory forensics is often viewed as an advanced, esoteric skill. However, Jamie Levy’s summit presentation aimed to democratize it by demonstrating practical, repeatable techniques using Volatility 3. Memory dumps contain a treasure trove of evidence—running processes, network connections, and even encrypted data—that is invisible to disk-based scanners.
Step‑by‑step guide to basic memory forensics:
Step 1: Acquire a memory dump.
Use tools like `dumpit` (Windows) or `avml` (Linux) to capture physical memory. For Linux:
sudo ./avml /path/to/output.mem
Step 2: Identify the operating system profile.
Volatility 3 can automatically detect the OS profile, but you can also specify it manually:
vol -f /path/to/dump.mem windows.info
Step 3: List running processes.
Look for hidden or suspicious processes:
vol -f /path/to/dump.mem windows.pslist
Step 4: Dump malicious processes.
If you identify a suspicious process (e.g., one with a mismatched name or path), dump its memory for further analysis:
vol -f /path/to/dump.mem windows.dumpfiles --pid 1234
Step 5: Scan for injected code.
Use the `malfind` plugin to detect memory regions with suspicious permissions (e.g., read-write-execute) that may indicate shellcode injection:
vol -f /path/to/dump.mem windows.malfind
AI can accelerate this process by automatically correlating process lists with known good baselines and flagging anomalies for human review.
- Avoiding Hunt Amnesia: Building a Memory Your AI Can Use
One of the most overlooked aspects of threat hunting is knowledge management. Sydney Marrone’s talk, “Avoiding Hunt Amnesia: Building a Memory Your AI Can Use,” addressed the challenge of retaining and reusing hunting insights. Many teams conduct excellent investigations but fail to document findings in a structured, machine-readable format. This means the same mistakes are repeated, and institutional knowledge is lost.
Step‑by‑step guide to building a hunt knowledge base:
Step 1: Standardize documentation.
Use a template that includes the hypothesis, data sources, queries run, findings, and remediation steps. Store these in a centralized repository (e.g., Confluence, GitHub).
Step 2: Tag and categorize.
Tag each hunt with MITRE ATT&CK techniques, affected systems, and severity levels. This enables easy retrieval and correlation.
Step 3: Feed findings into your AI agent.
Train your AI on past hunt reports so it can recognize patterns and suggest relevant queries for new investigations. For example, if you previously found credential dumping via lsass.exe, the AI can proactively recommend hunting for that technique when new lsass-related CVEs are published.
Step 4: Create automated playbooks.
Translate successful hunts into automated detection rules (e.g., Sigma, KQL) that can be deployed in your SIEM or EDR. This closes the loop from proactive hunting to continuous detection.
Step 5: Schedule regular retrospectives.
Review past hunts quarterly to identify gaps, refine hypotheses, and update your threat model. This ensures your hunting program evolves alongside the threat landscape.
What Undercode Say:
- Key Takeaway 1: Threat hunting is not a tool or a technology—it’s a mindset. The most effective hunters assume compromise, think like attackers, and use data-driven hypotheses to uncover stealthy adversaries. The summit reinforced that automation and AI are powerful enablers, but human intuition and contextual understanding remain irreplaceable.
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Key Takeaway 2: The integration of AI agents into threat hunting workflows is no longer theoretical—it’s happening now. From automated query generation to memory forensics and behavioral analysis, AI is reducing the barriers to entry and allowing smaller teams to punch above their weight. However, as Jason Haddix cautioned, securing the AI itself is paramount; attackers are already targeting AI pipelines, and defenders must treat AI agents as high-value assets requiring rigorous monitoring and protection.
Analysis: The Threat Hunting Summit 2026 represents a critical inflection point in cybersecurity. For years, the industry has been dominated by reactive tools—SIEMs, EDRs, and vulnerability scanners—that generate alerts but rarely provide context. The summit’s curriculum, spanning LOLBIN abuse, Linux persistence, AI agents, and memory forensics, signals a maturation of the discipline. Practitioners are no longer content with simply detecting known threats; they are building proactive programs that leverage every available data source, from network logs to physical memory dumps, and augmenting human expertise with artificial intelligence. The emphasis on affordable, hands-on training (courses priced at $295–$575) also addresses a longstanding industry gap: the high cost of advanced security education. By democratizing access to cutting-edge techniques, events like this are empowering a new generation of defenders to think like attackers and defend with confidence. The future of threat hunting lies in this synthesis of human creativity, AI augmentation, and continuous learning—a future that is already here.
Prediction:
- +1 The adoption of agentic AI in threat hunting will accelerate dramatically over the next 12–18 months, with major vendors embedding AI co-pilots into their SIEM and XDR platforms. This will lower the barrier to entry for junior analysts and reduce mean time to detection (MTTD) by 40–60% in mature organizations.
- +1 Open-source hunting tools like RITA, Osquery, and Volatility will see a resurgence as AI agents make them more accessible. Expect a new wave of community-driven hunting packages and playbooks that combine these tools with LLM-powered interfaces.
- -1 The increasing reliance on AI for threat hunting will create new attack surfaces. Adversaries will develop techniques to poison training data, manipulate AI outputs, and exploit agentic workflows. Organizations that fail to secure their AI pipelines will face novel, AI-driven breaches that bypass traditional defenses.
- -1 The skills gap in threat hunting will persist, but it will shift from technical expertise to strategic thinking. As AI automates query writing and log analysis, the premium will be on hypothesis generation, threat modeling, and incident response coordination—skills that require experience and judgment, not just tool proficiency.
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