Closing the visibility gap in vulnerability scanning.
Two autonomous AI agents that improve network scanning accuracy by 3× compared to traditional scanners and validate real exploitability with reproducible evidence.
The visibility gap
Problem Statement
Across 20 years of pentesting and 650+ client engagements, we saw the same pattern: scanners fingerprint confidently on paper, but real networks are messier than any signature database can cover.
Signature scanners lose accuracy at scale
Customized software, proxies, altered headers and non-standard ports reduce automated detection reliability, producing false negatives and misidentified services.
Known exploited. No proof available.
CISA flags a CVE as actively exploited in the wild. It falls within scope. There is no public proof-of-concept, no reference implementation, no reproducible environment. The finding either gets deprioritized or requires hours of original research to substantiate.
See what your scanner misses.
Given an open port, the agent interacts with the service, researches its behaviour online, and returns exactly what (and which version) is running. It catches services that signature-based scanners fingerprint wrong or miss entirely.
- Dynamic probing beyond static signatures.
- Transparent, auditable output: service, version, confidence score, references.
more accurate on average vs. Nessus Pro & Rapid7 IVM
more services detected in obfuscated networks
service identification accuracy in obfuscated networks
Figures are from internal benchmarks.
SSH-2.0-OpenSSH_8.9p1 banner returned on TCP connect.
AJP/1.3 FORWARD_REQUEST returned X-Powered-By: Servlet/4.0; version extracted from /WEB-INF/web.xml via attribute injection.
GET / redirected to /common/logon/logon.jsf; version string "GlassFish Server Open Source Edition 5.1.0" found in login page footer.
The ports above are illustrative examples. Accuracy figures are from internal benchmarks.
From theoretical CVE to reproducible PoC.
Given a software, version and CVE, the agent spins up a minimal containerised sandbox mimicking the vulnerable configuration, adapts public PoCs, and demonstrates real exploitability. A line in a report becomes evidence you can replay.
- Automated sandbox construction for the specific vulnerable configuration.
- A growing library of pre-built vulnerable sandboxes available to pentesters for immediate hands-on work.
reproducible sandbox environments
exploit success rate
average validation time per CVE
Validated, not estimated
Every CVE gets its own isolated sandbox.
For every CVE, we build a test environment where the exploit has already been proven to work. No virtual machine setup, no hunting for exploit code.
Isolated by design
Each sandbox is a fresh container with no access to your network.
One-command reproducibility
The packaged environment ships with a verify script. Any pentester can replay the exact exploit, on demand.
No public PoC needed
If no exploit exists publicly, the agent writes one. The validation pipeline runs regardless of whether the CVE has known PoC code.
The team
Built by pentesters who got tired of missing things.
650+ client engagements and 5,000+ reports across financial, telecommunication, public infrastructure and healthcare sectors. We automate the repetitive parts so experts can focus on the creative ones.
15+ years of pentesting experience across diverse industries, covering the full spectrum of security assessments. Conducts vulnerability research and has discovered multiple 0-day vulnerabilities. Three-time DEF CON presenter, exposing vulnerabilities in in-game scripting engines, the ANT/ANT+ protocols and math-related software suites.
- Public 0-days: Visual Mining NetCharts, MDaemon, D-Link DNS-321, ClamAV, BugTracker.NET, ManageEngine, Garmin Training Center, Garmin fitness watches, Maple, Mathematica, MATLAB, Unitrends
- Non-public: VMWare, NetIQ, Quest KACE
Cybersecurity AI agent researcher at Eötvös Loránd University. Earlier work spans natural-language processing and classical machine learning. Currently focused on autonomous red-team agents.
- HackSynth: LLM agent and evaluation framework for autonomous penetration testing
- Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges
- LlamBERT: Large-scale low-cost data annotation in NLP
Request access
Run ExploitSynth against your next engagement.
The beta is invite-only. Tell us about your environment and we'll reach out with credentials within a few days.