The threat landscape has shifted. Attackers are no longer just running automated scanners and hoping for the best. They are leveraging machine learning models to evade detection, fuzz APIs intelligently, and chain vulnerabilities with a level of precision that traditional tools cannot match. Security teams that are not keeping pace with this evolution are already behind.
At Redfox Cybersecurity, we work with red teams across industries who are actively integrating AI-assisted tooling into their engagements. This post covers the ten open-source tools that are actually showing up in real-world pentests right now, complete with commands, configurations, and technical context you can use immediately.
Manual testing still matters, but the attack surface has grown faster than team headcount. A modern enterprise application exposes hundreds of API endpoints, dynamic client-side logic, containerized microservices, and CI/CD pipelines, all of which require specialized probing. AI-augmented tools help testers prioritize, discover edge cases, and automate the cognitive grunt work so analysts can focus on what requires human judgment.
If you want to build structured offensive security skills around these tools, the Redfox Cybersecurity Academy offers hands-on courses designed for working security professionals, not just beginners chasing certifications.
GitHub: GreyDGL/PentestGPT
PentestGPT integrates large language models into the pentesting workflow by acting as a reasoning layer on top of your existing toolset. It does not replace Nmap or Burp Suite. It reads their output, reasons about what it means, and suggests next steps grounded in real attack methodology.
Setup and basic usage:
git clone https://github.com/GreyDGL/PentestGPT.git
cd PentestGPT
pip install -r requirements.txt
cp config.yaml.example config.yaml
# Add your OpenAI or local LLM endpoint in config.yaml
python pentestgpt.py --target 10.10.10.5 --mode reasoning
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Once running, you feed it scan results interactively. It maintains a session graph of what has been tested and what remains unexplored. For complex internal network engagements, this session awareness alone saves hours of manual note-keeping.
GitHub: projectdiscovery/nuclei
Nuclei is a fast, template-driven vulnerability scanner. What makes it relevant to this list is the growing ecosystem of AI-generated templates built using tools like NucleiGPT and community-trained models that generate valid YAML detection logic from CVE descriptions.
Running a targeted AI-generated template:
nuclei -u https://target.example.com \
-t ./ai-generated-templates/CVE-2024-XXXX.yaml \
-severity critical,high \
-rate-limit 50 \
-o results.json \
-json
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Sample AI-generated template structure for a SSTI payload:
id: ssti-jinja2-detect
info:
name: Jinja2 SSTI Detection
severity: high
http:
- method: GET
path:
- "{{BaseURL}}/search?q={{7*7}}"
matchers:
- type: word
words:
- "49"
part: body
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You can generate templates like this automatically by feeding CVE advisories into a local LLM with the right prompt structure. The Redfox Cybersecurity Academy covers custom Nuclei template development as part of its web application testing curriculum.
Burp Suite's Bambda scripting layer, combined with community extensions that hook into local LLMs, allows you to generate context-aware payloads based on parameter names, response patterns, and application behavior.
Example Bambda filter for identifying JWT-bearing responses:
if (requestResponse.response() != null) {
String body = requestResponse.response().bodyToString();
return body.contains("eyJ") &&
requestResponse.response().statusCode() == 200;
}
return false;
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Pair this with an LLM-backed extension such as BurpGPT to automatically generate injection payloads based on parameter context. When you find a parameter named template_name, the model infers it may be vulnerable to SSTI and generates appropriate payloads rather than firing a generic wordlist.
For red teams looking to go beyond Burp's defaults, Redfox Cybersecurity's offensive web testing services incorporate this kind of context-aware payload generation into client engagements.
GitHub: frida/frida
Frida is a dynamic instrumentation toolkit used heavily in mobile and thick client testing. The AI angle here is using ML models to recommend hook points based on decompiled code analysis, instead of manually auditing every method.
Basic Python Frida hook for Android SSL pinning bypass:
import frida, sys
def on_message(message, data):
print("[{}] => {}".format(message, data))
jscode = """
Java.perform(function () {
var SSLContext = Java.use("javax.net.ssl.SSLContext");
SSLContext.init.overload(
"[Ljavax.net.ssl.KeyManager;",
"[Ljavax.net.ssl.TrustManager;",
"java.security.SecureRandom"
).implementation = function(km, tm, sr) {
console.log("[*] SSLContext.init called - bypassing pinning");
this.init(km, null, sr);
};
});
"""
process = frida.get_usb_device().attach("com.target.app")
script = process.create_script(jscode)
script.on("message", on_message)
script.load()
sys.stdin.read()
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When combined with tools like Ghidra's headless analyzer and an LLM summarizing decompiled output, you can identify hook targets in large binaries significantly faster than manual review.
GitHub: returntocorp/semgrep
Semgrep is a static analysis engine that supports pattern matching across dozens of languages. AI models can generate Semgrep rules from vulnerability descriptions or from code samples you flag as dangerous.
Running a custom AI-generated rule against a Node.js codebase:
semgrep \
--config ./ai-rules/nodejs-sqli.yaml \
--output results.sarif \
--sarif \
./target-app/src
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Sample rule targeting unsafe query concatenation:
rules:
- id: sqli-string-concat
patterns:
- pattern: |
$DB.query("..." + $INPUT)
message: >
Potential SQL injection via string concatenation.
Use parameterized queries instead.
languages: [javascript]
severity: ERROR
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During source code review engagements, Redfox Cybersecurity uses Semgrep as the first pass to surface patterns at scale before manual auditors dig into the high-confidence findings. Learn how this fits into a full SAST methodology at Redfox Cybersecurity's services page.
GitHub: Tib3rius/AutoRecon
AutoRecon automates the reconnaissance phase by running dozens of enumeration tools in parallel and organizing output by service type. Its value multiplies when you pipe its structured output into an LLM that reasons about what each open service implies for the attack surface.
Basic invocation against an internal target:
autorecon 10.10.10.50 \
--single-target \
--output ./recon-output \
--only-scans-dir \
-v
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After the scan completes, a Python wrapper feeding the service summary to a local Ollama instance can produce a prioritized attack plan based on the discovered services, operating system fingerprint, and software versions, without sending sensitive client data to a third-party API.
import ollama
with open("./recon-output/10.10.10.50/_commands.log") as f:
scan_data = f.read()
response = ollama.chat(
model="llama3",
messages=[{
"role": "user",
"content": f"Given this recon output, list the top 5 attack vectors ranked by exploitability:\n\n{scan_data}"
}]
)
print(response["message"]["content"])
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GitHub: projectdiscovery/katana
Katana is a next-generation web crawler built for offensive security. With custom field extraction and headless browser support, it maps application logic far more accurately than legacy crawlers. AI can be layered on top to prioritize which discovered paths are worth probing for injection, IDOR, or access control issues.
Crawling with headless mode and custom field extraction:
katana -u https://app.target.com \
-jc \
-d 5 \
-ef css,png,jpg,woff \
-o endpoints.txt \
-headless \
-rate-limit 20
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Feeding the resulting endpoint list into a classifier trained on labeled vulnerability data lets you rank URLs by their likelihood of containing an injectable parameter, well before a scanner fires a single payload.
Website: caido.io
Caido is a modern web proxy that is increasingly being adopted by professional red teams as a Burp alternative. Its workflow automation engine accepts JavaScript logic that can call external AI APIs to generate payloads on the fly during active proxying sessions.
Example Caido workflow that calls a local LLM to mutate a captured request:
export async function onRequest(request) {
const body = await request.text();
const response = await fetch("http://localhost:11434/api/generate", {
method: "POST",
body: JSON.stringify({
model: "mistral",
prompt: `Mutate this HTTP body to test for XSS and SSTI:\n${body}`,
stream: false
})
});
const result = await response.json();
return new Request(request.url, {
method: request.method,
headers: request.headers,
body: result.response
});
}
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This approach turns every proxied request into a testing opportunity without requiring manual payload selection.
GitHub: calebstewart/pwncat
Pwncat-CS is a post-exploitation framework that handles shell stabilization, file transfer, privilege escalation enumeration, and persistence. The AI enhancement comes from chaining its enumeration output into a model that recommends specific escalation paths.
Connecting to a reverse shell via pwncat:
pwncat-cs -l -p 4444
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Running built-in enumeration and exporting for AI analysis:
# Inside pwncat-cs interactive session
run enumerate.system
run enumerate.software
run enumerate.creds
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The structured output from these modules can be fed directly into a local LLM with a system prompt tuned for Linux privilege escalation reasoning. You get back a prioritized list of escalation paths specific to the target environment rather than a generic checklist.
For teams wanting to operationalize this kind of AI-assisted post-exploitation in real engagements, Redfox Cybersecurity's red team services offer end-to-end adversary simulation with documented methodology.
GitHub: leondz/garak
As AI systems become targets themselves, security teams need tools to probe them. Garak is an LLM vulnerability scanner that tests language models for prompt injection, data leakage, jailbreaks, and insecure output handling. If your client has deployed an AI chatbot or LLM-integrated application, this belongs in your toolkit.
Running a probe suite against a locally hosted model:
pip install garak
python -m garak \
--model_type huggingface \
--model_name "mistralai/Mistral-7B-Instruct-v0.2" \
--probes promptinject,knownbadsignatures,encoding \
--report_prefix ./garak-results
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Running against an OpenAI-compatible API endpoint:
python -m garak \
--model_type openai \
--model_name gpt-4o \
--probes dan,gcg,tap \
--extended_detectors \
--report_prefix ./results/gpt4o-audit
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Garak's modular probe architecture means you can write custom probes targeting business-specific AI behaviors, such as an AI customer service agent that should never reveal pricing logic or internal system prompts. This is an area where the Redfox Cybersecurity Academy is actively developing new course material on AI application security testing.
Using any one of these tools in isolation gives you marginal gains. The real force multiplication happens when you integrate them into a coherent workflow:
This is the workflow Redfox Cybersecurity's red team operators are refining through real client engagements. If you want your internal team to develop proficiency across this stack, the Redfox Cybersecurity Academy provides structured lab environments and instructor-led paths built around exactly these tools.
The integration of AI into offensive security tooling is not a trend to watch; it is a capability gap that separates high-performing security teams from those still running checkbox assessments. The ten tools covered here are all actively maintained, battle-tested in real engagements, and genuinely benefit from AI augmentation rather than using machine learning as a marketing label.
Start with the tools that fit your most common engagement type, build local LLM integrations using Ollama to keep client data off third-party APIs, and invest in the systematic training that turns individual tool proficiency into team-wide methodology.
For professional red team support or to evaluate your organization's security posture with these techniques applied by experienced operators, reach out to Redfox Cybersecurity to discuss your next engagement.
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