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Introduction:
Modern cybersecurity research demands rapid synthesis of thousands of threat reports, vulnerability disclosures, and academic papers. Traditional manual methods fail to keep pace with the volume of emerging exploits. By integrating nine specialized AI tools—Perplexity, Scite, Elicit, ChatGPT, Scholarcy, Consensus, ResearchRabbit, Litmaps, and Semantic Scholar—you can automate source verification, citation mapping, and real‑time intelligence gathering, transforming raw data into actionable security insights.
Learning Objectives:
- Automate threat intelligence collection and source validation using AI‑powered research assistants.
- Build command‑line workflows for batch summarization and citation visualization of CVE databases.
- Integrate academic AI tools with SIEM platforms to correlate exploit literature with live attack patterns.
You Should Know:
1. Perplexity.ai – Real‑Time Threat Intelligence Queries
Perplexity.ai combines live web search with LLM summarization, making it ideal for tracking emerging zero‑days. Use its API to feed fresh IOCs (indicators of compromise) directly into your analysis pipeline.
Step‑by‑step guide:
- Obtain an API key from perplexity.ai (free tier available).
- Query the latest CVE trends with a `curl` command (Linux/macOS):
curl -X POST https://api.perplexity.ai/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"llama-3.1-sonar-small-128k-online","messages":[{"role":"user","content":"List top 5 most exploited vulnerabilities this week with affected software versions"}]}' | jq '.choices[bash].message.content'
3. On Windows (PowerShell), replace `curl` with `Invoke-RestMethod`:
$body = @{model="llama-3.1-sonar-small-128k-online"; messages=@(@{role="user"; content="List top 5 most exploited vulnerabilities this week with affected software versions"})} | ConvertTo-Json
Invoke-RestMethod -Uri "https://api.perplexity.ai/chat/completions" -Method Post -Headers @{Authorization="Bearer YOUR_API_KEY"; "Content-Type"="application/json"} -Body $body | Select-Object -ExpandProperty choices | Select-Object -ExpandProperty message | Select-Object -ExpandProperty content
4. Pipe the output to a log file and trigger a SIEM alert if unpatched critical CVEs are detected.
5. Schedule the query via cron (Linux) or Task Scheduler (Windows) for hourly threat updates.
- Scite.ai – Verifying Exploit Claims with Citation Context
Scite.ai reveals how research papers are cited—supporting, contrasting, or mentioning—which is critical for debunking fake PoC exploits.
Step‑by‑step guide:
- Register at scite.ai and generate an API key.
2. Install Python and the `requests` library:
pip install requests
3. Create a validation script `verify_exploit.py`:
import requests, sys
api_key = "YOUR_SCITE_KEY"
paper_doi = sys.argv[bash] DOI of the exploit paper
url = f"https://api.scite.ai/reports?doi={paper_doi}&key={api_key}"
response = requests.get(url).json()
supporting = response['total_citations']['supporting']
contrasting = response['total_citations']['contrasting']
print(f"Supporting citations: {supporting}, Contrasting: {contrasting}")
if contrasting > supporting:
print("WARNING: Exploit claims are likely disputed.")
4. Run the script on any CVE‑referenced DOI: python verify_exploit.py 10.1109/SP.2024.12345.
5. Integrate with your vulnerability management tool (e.g., DefectDojo) to auto‑flag contested exploits.
3. Elicit.org – Batch Summarization of Breach Reports
Elicit automates extraction of key findings from forensic PDFs. Use its bulk mode to process hundreds of incident reports.
Step‑by‑step guide:
- Access elicit.org (free academic tier) and upload a folder of breach analysis PDFs.
- For command‑line automation, use the unofficial `elicit‑cli` (Node.js):
npm install -g elicit-cli elicit login --api-key YOUR_KEY elicit summarize --input ./breach_reports/ --output ./summaries/ --format json
- Parse the JSON summaries with `jq` to extract attack vectors:
cat summaries/.json | jq '.[] | .attack_vector' | sort | uniq -c
- Feed the aggregated vectors into MITRE ATT&CK mapping scripts (using `attack‑cti` Python library).
- Schedule weekly batch runs to keep your threat model current.
4. ChatGPT – Explaining Obfuscated Malware Code
ChatGPT’s API can deobfuscate and explain malicious scripts, accelerating reverse engineering.
Step‑by‑step guide:
- Get an OpenAI API key from chatgpt.com (platform.openai.com).
2. Create a Python deobfuscator `chatgpt_analyze.py`:
import openai
openai.api_key = "YOUR_OPENAI_KEY"
with open("malware_sample.ps1", "r") as f:
code = f.read()
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a malware analyst. Deobfuscate and explain each function."},
{"role": "user", "content": code}
]
)
print(response.choices[bash].message.content)
3. Run against suspicious PowerShell or VBScript files: python chatgpt_analyze.py.
4. For Windows batch processing, wrap the script in a `.bat` file looping over `.ps1` files in a sandboxed folder.
5. Never send live corporate secrets—use a sanitised clipboard for manual queries.
- Scholarcy – Auto‑Generating Vulnerability Summaries from CVE JSON
Scholarcy extracts structured summaries from academic papers. Combine it with NVD’s CVE feed to auto‑write short vulnerability profiles.
Step‑by‑step guide:
- Sign up at scholarcy.com for API access.
- Download the latest CVE JSON from NVD: `wget https://nvd.nist.gov/feeds/json/cve/2.0/nvdcve-2.0-2026.json.zip`
- Extract and parse with `jq` to feed CVEs into Scholarcy:
unzip nvdcve-2.0-2026.json.zip cat nvdcve-2.0-2026.json | jq '.vulnerabilities[bash].cve.id' | head -10 > cve_ids.txt
4. Use the Scholarcy Python client (install
scholarcy‑api):from scholarcy import ScholarcyClient client = ScholarcyClient(api_key="YOUR_SCHOLARCY_KEY") for cve_id in open("cve_ids.txt"): summary = client.summarize_text(f"Explain {cve_id} including attack vector and CVSS score") print(f"{cve_id.strip()}: {summary}") - Append summaries to your internal KB for rapid SOC triage.
-
ResearchRabbit & Litmaps – Visualizing Citation Chains of Attack Patterns
These tools map how exploits evolve over time. Use Litmaps to build a graph of connected vulnerability research and ResearchRabbit to recommend undiscovered PoC papers.
Step‑by‑step guide:
- Create free accounts at researchrabbit.ai and litmaps.com.
- In Litmaps, enter a seed paper (e.g., “Spectre attack” DOI). Export the citation graph as a `.csv` of edge lists.
- Use Gephi or Python’s `networkx` to visualise clusters:
import networkx as nx, pandas as pd df = pd.read_csv("litmaps_edges.csv") G = nx.from_pandas_edgelist(df, 'source', 'target') nx.write_gexf(G, "attack_chain.gexf") - In ResearchRabbit, upload your known references; the tool suggests “missed” related papers—often containing alternative exploitation methods.
-
Fuzz those titles with Shodan to identify real‑world vulnerable devices.
-
Semantic Scholar – Deep Paper Search for AI Security Hardening
Semantic Scholar’s API returns papers on adversarial machine learning, model extraction, and defensive distillation. Use it to harden your AI pipelines.
Step‑by‑step guide:
- Obtain a free API key from semanticscholar.org.
2. Query for adversarial ML papers with `curl`:
curl "https://api.semanticscholar.org/graph/v1/paper/search?query=adversarial%20example%20robustness&limit=10&fields=title,abstract,year,citationCount" | jq '.data[] | {title:.title, citations:.citationCount}'
3. Automate downloading of top‑cited papers using `wget` and their `openAccessPdf` links.
4. Run a local LLM (e.g., Ollama with Llama 3) to extract mitigation techniques from the abstracts:
cat abstracts.txt | ollama run llama3 "Summarize all defense methods against evasion attacks"
5. Implement the most promising method (e.g., adversarial training or input preprocessing) in your production AI models.
What Undercode Say:
- Key Takeaway 1: You don’t need expensive threat intelligence platforms—open‑source AI tools combined with simple shell scripts can automate 80% of your research pipeline.
- Key Takeaway 2: Verifying sources (Scite.ai) and visualising citation relationships (Litmaps) turns academic noise into a directed attack graph, revealing hidden zero‑day links.
- Key Takeaway 3: Every AI research tool is also a potential API for automation; treat API keys as credentials, store them in environment variables, and never hardcode into shared scripts.
Prediction:
Within 18 months, mature security operations centers will replace junior analyst roles with AI‑agent pipelines built from tools like Perplexity and Scholarcy. However, the same tools will empower red teams to fabricate convincing fake research citations, forcing blue teams to adopt blockchain‑anchored verification layers. The arms race will shift from data collection to synthetic data detection—your ability to write custom API glue code will become your primary competitive advantage.
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