1. Can AI replace ethical hackers?
2. How pentesters can use AI to help them in their work?
3. Can AI analyze output of the LinPeas.sh script?
The 3rd question is an example, it shows how AI can be interacted with when a specific, tedious task is to be done.
AI can augment human pentesters, to hasten some of their tedious work.
AI can, for example, be used to parse and interpret output of the LinPEAS.sh script, saving pentester a lot of time and effort during the post-exploitation phase, when one tries to escalate his or her privileges.
So, humans should remain in control, be the 'pentest leaders', that use AI for help in some of their tedious tasks.
To do their work well, however, the 'pentest leaders' should know & understand the nuances of the cybersecurity fields, especially the pentesting parts. We should be aware that AI will sometimes fail, and should be able to do some things manually, when this happens.
This can be very good news for pentesters, as human pentesters who will adapt to use AI in their work will be competitive, and will remain competitive in the job market.
As the History teaches us,
During the Times of Great Changes, those who Adapt will come Victorious,
Those who Oppose Changes will be left behind.
Can AI Replace Ethical Hackers?
Answer:
That’s a deep question. Let’s break it down:
Ethical hackers (or penetration testers) simulate cyberattacks on systems to uncover vulnerabilities before malicious actors exploit them. Their role isn’t just technical — it also involves:
- Understanding business contexts and risks,
- Thinking creatively and unpredictably, like an attacker would.
- Communicating findings clearly to stakeholders.
- Advising on remediation strategies and balancing security with usability.
2. What AI can do today?
AI is already being applied in security testing and operations:
- Automation: AI can scan for known vulnerabilities, misconfigurations, and patterns at scale
much faster than humans.
- Exploit simulation: Some AI systems can chain together attack paths or fuzz inputs to find
weaknesses.
- Threat intelligence: AI can analyze vast amounts of threat data, flag anomalies, and
predict possible attack vectors.
- Red teaming tools: Emerging AI-driven platforms can mimic attacker behaviors and adapt
in real time.
3. Limitations of AI:
- Contextual judgment: AI struggles to weigh risks in a business or regulatory context.
For example, it might find a vulnerability but can’t decide if it’s truly critical to
that company’s operations.
- Creativity: Real attackers innovate — they don’t just follow known patterns. Humans excel
at thinking "outside the box" in ways AI models don’t yet replicate.
- Ethics & responsibility: Deciding how to responsibly disclose vulnerabilities or balance
customer trust is still very human.
- Adversarial risk: Attackers can manipulate AI-driven systems, feeding them misleading data
or evading detection.
4. Likely future.
Instead of replacing, AI will augment ethical hackers:
- AI will handle repetitive scanning, data analysis, and simulation.
- Hackers will focus on creative exploitation, strategy, and interpretation.
- The partnership could make assessments faster, deeper, and more accurate.
Bottom line:
AI won’t replace ethical hackers, but it will significantly change their role.
Ethical hackers of the future will need to work with AI, guiding it and applying human insight, where machines fall short.
I am a pentester. how can i use AI to help in my work?
Answer:
That’s a great question — AI can be a real force-multiplier in penetration testing if you know how to harness it. Here are some ways you can integrate AI into your workflow, broken down by phase of a typical engagement:
- OSINT automation: Use AI to parse large sets of WHOIS, Shodan, DNS, LinkedIn, or GitHub
data, quickly extracting relationships (employees, emails, exposed repos, tech stack).
- Text summarization: Feed in scraped content (press releases, job postings) and let AI
highlight likely internal systems, frameworks, or partners.
- Entity extraction: Train or prompt AI to spot emails, IPs, subdomains, or sensitive
strings from raw data dumps.
2. Scanning & Enumeration.
- Log interpretation: Paste Nmap or Nessus results and let AI group findings by
exploitability, prioritize by CVSS, or map to MITRE ATT&CK.
- Banner analysis: Feed AI strange service banners or version info; it can suggest likely
vulnerabilities, known exploits, or misconfigurations.
- Protocol fuzzing hints: AI can propose fuzzing payloads for edge-case input (common in
web apps, APIs, IoT).
3. Exploitation & Payload Crafting.
- Exploit adaptation: AI can help rewrite public PoCs to target slightly different
environments.
- Payload obfuscation ideas: Get suggestions for bypassing WAFs, IDS, or AV by altering
encodings, command structures, or macros.
- Reverse engineering aid: Summarize decompiled code, point out suspicious functions, or
hypothesize attack vectors.
4. Post-Exploitation & Reporting.
- Log analysis: Run harvested logs through AI to surface credentials, tokens, or lateral
movement opportunities.
- Privilege escalation guidance: Describe the host/system state, and AI can propose likely
misconfigurations or kernel exploits.
- Report writing: AI excels at turning raw notes into clear, professional findings with
executive summaries and technical detail (saves huge time).
5. Practical AI Uses in Daily Pentesting.
- Custom tool generation: Quickly script wrappers around APIs (Shodan, VirusTotal, Censys)
or automation for repetitive tasks.
- Cheat sheets: Ask AI for command snippets (Linux privilege escalation, PowerShell
one-liners, SQLi payloads).
- Knowledge distillation: Instead of digging through dozens of CVE writeups, AI can summarize
exploit requirements & impact.
Important note:
Always stay within legal scopes. AI can accelerate exploit generation or payload crafting, but don’t let it tempt you into going outside of your engagement boundaries.
Can AI analyze output of the LinPeas.sh script?
Answer:
Yes — AI can absolutely analyze the output of LinPEAS (LinPEAS.sh).
- Summarizing findings → Highlighting the most critical issues (e.g., SUID binaries,
writable cron jobs, weak sudo rules).
- Categorizing risks → Grouping findings by type (kernel exploits, file permissions,
network services, credentials, etc.).
- Explaining implications → For example, why a writable /etc/passwd is dangerous, or how
a misconfigured sudo rule could lead to root.
- Suggesting next steps → Recommending specific exploits or checks to attempt based on
the findings.
For example:
- If LinPEAS finds sudo -l allows running /usr/bin/vim without a password, AI could explain
that you can escalate to root via :!/bin/sh.
- If it detects a kernel version with a known exploit, AI can point you to the relevant
CVE and PoC.
If you share a snippet or full LinPEAS output here (you can redact sensitive info if needed), I can parse it and highlight the potential privilege escalation paths for you.
Do you want me to show you how I would analyze a sample LinPEAS output, or would you like to upload your script output for me to review directly?
[ my answer ]: not for now, but in future - probably yes.
Got it — whenever you’re ready, you can drop the LinPEAS output here, and I’ll go
through it to:
- Highlight the critical issues instead of leaving you with a giant wall of text.
- Explain what each finding means in plain language.
- Suggest possible exploitation paths (always with the caveat that you should only test
on systems you’re authorized to).
That way, instead of manually scanning hundreds of lines, you’ll get a prioritized list of "things worth looking at."
(...)
