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Introduction:
In the labyrinthine world of cyber threat intelligence, the critical skill that separates novice analysts from elite hunters is the art of pivoting. Pivoting is the iterative process of using a single piece of data—an IP address, a domain name, a certificate hash—to discover new, related pieces of information, thereby mapping the hidden infrastructure of threat actors. Alexandre Dulaunoy’s open-source book, “The Art of Pivoting,” codifies field-tested strategies to move beyond basic indicators of compromise (IoCs) and uncover sophisticated relationships through both classic and unconventional data points, fundamentally changing the scope and success of investigations.
Learning Objectives:
- Master the methodology of iterative pivoting across technical, human, and contextual domains.
- Learn to leverage often-overlooked data points like TLS certificates, passive DNS records, and historical whois data for discovery.
- Apply practical command-line and tool-based techniques to automate and enhance pivoting in real-world threat intelligence workflows.
You Should Know:
1. The Pivoting Mindset: Moving Beyond Linear Analysis
Pivoting is not a tool, but a foundational methodology. It requires shifting from a linear, checklist-driven investigation to a dynamic, relationship-based exploration. The core principle is that every piece of data contains embedded connections to other entities. A malware sample isn’t just a hash; it may contact a C2 domain, which is hosted on an IP, which shares a TLS certificate with ten other domains, one of which is registered by an email address found in another campaign. The analyst’s goal is to trace these “lines,” as visually inspired by Fabienne Verdier’s art, between seemingly separate points.
Step‑by‑step guide:
- Start with Your Initial Indicator: Begin with any IoC (e.g.,
malicious-sample.exe). - Extract Observable Entities: Use static analysis or sandbox reports to extract embedded data: file hash, C2 IP (
185.199.110.153), dropped file name. - Choose Your First Pivot Point: Select the most promising entity. An IP address is often a rich starting point.
- Query and Expand: Query the IP in threat intelligence platforms (TIPs), passive DNS, and SSL certificate databases. Discover it hosts
bad-domain.com. - Pivot Again: Now take `bad-domain.com` and query its whois history, nameservers, and SSL certificate. Find the certificate SHA-1 fingerprint.
- Pivot on the Certificate: Search certificate transparency logs or Shodan for other domains sharing that exact certificate. You may discover `decoy-site.org` and
payload-delivery.net. - Document the Graph: Use a tool like Maltego or even a simple diagram to visually map the connections between all discovered entities. This graph is your intelligence asset.
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Pivoting on Core Technical Indicators: IPs, Domains, and DNS
This is the bread and butter of technical pivoting. Each layer of network interaction provides a new pivot opportunity.
Step‑by‑step guide with commands:
Passive DNS Pivoting: Discover all historical domain names associated with an IP.
Using 'dig' for forward/reverse lookups dig +short A bad-domain.com Get IP dig +short -x 185.199.110.153 Reverse DNS (PTR record) Using a service like SecurityTrails or VirusTotal CLI (conceptual) This reveals all domains that have ever resolved to this IP.
WHOIS History Pivoting: Registration details are gold. An email, phone number, or name can link multiple domains.
Linux: whois command whois bad-domain.com | grep -i "registrant|admin|tech|email" Analyze output for email (e.g., [email protected]). Search this email across other whois records.
Nameserver Pivoting: Threat actors often use the same malicious nameservers for multiple domains.
dig +short NS bad-domain.com Returns: ns1.evil-hoster.com, ns2.evil-hoster.com Now find all domains using ns1.evil-hoster.com via platforms like RiskIQ or Shodan.
3. Unconventional Pivots: TLS Certificates and Cryptographic Artefacts
SSL/TLS certificates are a massively underutilized pivot. Actors often reuse certificates across infrastructure, or make telling mistakes in their configuration.
Step‑by‑step guide:
- Fetch the Certificate: From a domain or IP.
OpenSSL command to fetch certificate details openssl s_client -connect bad-domain.com:443 -servername bad-domain.com 2>/dev/null | openssl x509 -noout -text -fingerprint
- Extract Pivotable Fields: Note the
SHA-1 Fingerprint,Subject Alternative Names (SANs),Issuer, andValidity Period. - Pivot via Certificate Transparency (CT) Logs: Use tools like `crt.sh` to find all certificates with a matching subject, issuer, or, most powerfully, the same public key.
Visit: `https://crt.sh/?q=%.bad-domain.com` or `https://crt.sh/?q=`
4. Pivot via Scanners: Use Censys or Shodan to search for hosts presenting the same certificate fingerprint.Shodan CLI example (conceptual query) shodan search ssl.cert.fingerprint.sha1:<FINGERPRINT>
4. Pivoting on Malware Artefacts and Attack Patterns
When technical infrastructure is ephemeral, pivot on behaviors and code.
Step‑by‑step guide:
- Static Analysis Pivots: Extract hardcoded strings, API hashes, campaign IDs, or unique mutex names from malware.
- Dynamic Analysis Pivots: From sandbox reports, extract network call patterns, user-agent strings, or C2 protocol signatures.
- YARA Rule Pivoting: Write a YARA rule for a unique code snippet or technique. Search internal and shared repositories (like VirusTotal Intelligence) for other samples triggering this rule, linking disparate campaigns.
rule apt32_tool_foo { meta: author = "Your CTI Team" strings: $a = {6A 40 68 00 30 00 00 6A 14 8D 91} $b = "InternalCryptoTool_v1.2" wide condition: all of them } -
Automating Pivots with Threat Intelligence Platforms (TIPs) and APIs
Manual pivoting is slow. Automation is key.
Step‑by‑step guide:
- Leverage TIPs like MISP: Automatically enrich events. A single IP can trigger lookups that populate related domains, hashes, and email addresses from connected feeds.
- Script Common Pivots: Use Python with APIs from VirusTotal, PassiveTotal, Shodan, etc.
import requests vt_api_key = 'YOUR_KEY' ip = '185.199.110.153' url = f'https://www.virustotal.com/api/v3/ip_addresses/{ip}/resolutions' headers = {'x-apikey': vt_api_key} response = requests.get(url, headers=headers) Parse JSON response to get historical domains - Build a Pivoting Workflow: Chain API calls: IP -> (VirusTotal -> Passive DNS Domains) -> (Domain -> crt.sh Certificate Search) -> (Certificate Fingerprint -> Shodan Host Search).
What Undercode Say:
- Pivoting Transforms Data into Intelligence: A list of IoCs is just data. Understanding the connective tissue between them through pivoting creates actionable intelligence, revealing campaign scope, actor habits, and infrastructure lifecycles.
- The Uncommon Data Point is Your Advantage: While every analyst checks Virustotal for a hash, the winner will be the one who consistently checks SSL certificate SANs, nameserver history, or TLS JA3 fingerprints. Dulaunoy’s “Inventory of Uncommon Data Points” is a critical checklist for advanced hunting.
Analysis:
The publication of this open-source book marks a significant move towards democratizing advanced threat intelligence tradecraft. By focusing on methodology over specific tools, it ensures relevance despite the changing toolscape. The emphasis on “unconventional indicators” is a direct counter to sophisticated threat actors who rotate primary infrastructure but slip up on ancillary details like certificate reuse or domain registration patterns. The true value lies in systematizing what expert analysts do intuitively, creating a teachable, scalable process. This accelerates the development of junior analysts and enhances the consistency of team output. Integrating these pivoting techniques with emerging AI that can suggest novel pivot paths or visualize complex relationship graphs will represent the next evolution in investigative efficiency.
Prediction:
Within the next 2-3 years, pivoting will evolve from a manual, analyst-driven process to a semi-autonomous, AI-augmented function embedded directly within TIPs and SIEMs. Machine learning models will proactively suggest high-probability pivot paths based on global threat data and learned actor behaviors, predicting hidden connections before an analyst manually spots them. Furthermore, offensive security teams (red teams/penetration testers) will increasingly adopt these same pivot methodologies to better emulate advanced persistent threats (APTs), discovering exposed internal assets and trust relationships during engagements, making defensive mastery of these techniques even more critical.
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Extra Hub: Undercode MoN
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