ThreatIntel-Reports aggregates raw content extracted from thousands of threat intelligence reports and lets authorized analysts search it by keyword through a web UI or local Python scripts.
| Tool | mthcht/ThreatIntel-Reports — repository of extracted content from thousands of threat intelligence reports with automatic collection and keyword search |
| Category | Cyber threat intelligence (CTI) aggregation and search |
| Primary Use | Searching thousands of collected threat intelligence reports by keyword, via the hosted site or the local search_keywords*.py scripts, for threat hunting and malware research |
| Safe Use | Purely defensive research and authorized threat intelligence work; it indexes public reporting and contains no exploitation capability |
| Telemetry Note | Defenders leave no footprint on third-party systems; the only observable activity is HTTP fetching of public report feeds by the automatic collector |
ThreatIntel-Reports, published by mthcht on GitHub, is a Python-based cyber threat intelligence resource that takes a deceptively simple idea and executes it at scale: extract the raw textual content from thousands of published threat intelligence reports, store it in one place, and make all of it searchable by keyword. Rather than building yet another curated feed or a paid platform, the project treats the public output of vendors, CERTs, and independent researchers as a corpus to be mined. With 172 stars and a topic list spanning cyber-threat-intelligence, threat-hunting, malware-analysis, and apt, it occupies a niche aimed squarely at working analysts who need recall across historical reporting, not just the latest headlines.
The core value proposition is coverage. When an analyst encounters an unfamiliar tool name, malware family, or actor alias during an investigation, the usual workflow is a scattergun of search engine queries across dozens of vendor blogs, each with its own indexing quirks and paywalls. ThreatIntel-Reports collapses that into a single keyword lookup against the full extracted text of thousands of reports simultaneously. That transforms IOC enrichment and TTP research from an archaeology exercise into a grep-style operation, which is exactly the workflow the topic tags like threat-modeling and threat-analysis imply the author built it for.
There are two consumption paths documented in the README. The first is the hosted GitHub Pages site at mthcht.github.io/ThreatIntel-Reports, which exposes a search bar over predefined keyword result sets. The author notes this index is expanding to cover over 2000 tools and a selection of APT groups, and explicitly invites requests for additional default keywords. The second path is local: clone the repository and run one of the search_keywords*.py scripts, which prompts for one or multiple comma-separated keywords, sweeps the entire report corpus, and writes the matching results to a JSON file. That JSON output is deliberately structured so results can be folded back into the site's index, meaning local findings can become part of the shared UI dataset.
The dual CLI/web architecture is worth pausing on, because it reveals the project's design philosophy. The hosted site is a convenience layer over precomputed results, optimized for quick lookups and discovery. The CLI scripts are the raw engine: they search everything, not just the curated keyword sets, and they run entirely on your own clone of the data. For an analyst in a restricted environment, or one whose keyword of interest is an internal codename that would never appear in the public preset list, the local path is the one that matters. The JSON result format also makes the output trivially scriptable for downstream enrichment pipelines.
Automation is the other half of the project's subtitle, and the README refers to it as automatic extraction of reports from various feeds. In practical terms, this means the corpus is not a static snapshot but a continuously refreshed collection: new public reports are pulled in as they appear, their content extracted, and the searchable body of data grows over time. For a defensive team, that temporal dimension matters, because threat reporting ages quickly and a search index that is months stale is only marginally more useful than no index at all. The repository's main branch is the live artifact of that pipeline.
The topic tags give a good picture of the intended audience and use cases. cti and threatintel mark it as a cyber threat intelligence workbench; malware-analysis-reports and malware-research point at malware family research, where tracing how different vendors described the same sample over the years is genuinely valuable; apt signals coverage of state-aligned actor reporting, where alias resolution across vendors is a chronic pain point. threat-hunting rounds out the list, suggesting the corpus can be mined for the tool names, registry paths, and behavioral indicators that inform detection engineering and hunt hypotheses.
From a defensive tradecraft perspective, this is a force multiplier for detection and research teams. Consider the recurring task of building context around a lolbas binary, a C2 framework, or a credential-dumping tool observed in a customer environment during an authorized assessment or incident response engagement. Instead of manually reconstructing which reports mention it, the analyst gets an immediate map of every report in the corpus that references the keyword, effectively a bibliography of primary sources to read next. That same corpus is useful in the other direction too: pulling all reports mentioning a given APT group to study tooling overlaps and historical evolution of tradecraft.
Because the corpus is raw extracted report content rather than summaries, the analyst is always reading the original vendor text, which sidesteps the lossiness that curated taxonomies like MITRE ATT&CK mappings introduce. The tradeoff is that the search is literal: keyword matching over report text will return noise, false positives on common words, and hits that mention a tool only in passing. Skilled users will treat the results as a candidate list to triage, not a verdict, and will prefer specific keywords — a malware family name, a distinctive utility name, an actor alias — over generic terms.
There is an operational subtlety worth noting for anyone running the local workflow. Cloning the repository means pulling down the entire extracted corpus, which has both a size cost and a content consideration: threat reports describe malicious tooling in detail, so the local dataset is inherently sensitive material to handle responsibly, best kept inside a research VM or analyst workstation. The search_keywords*.py scripts themselves are straightforward Python, easy to audit before running, which is good hygiene for any tool that will live on an analyst box.
The project's defensibility is also its charm: it contains no exploit code, no payloads, and no offensive capability whatsoever. It is an indexing and retrieval layer over public defensive reporting, which makes it suitable for any environment from a SOC to a university research group. The only network behavior associated with the project is the upstream collector fetching public report feeds, and the hosted search UI making ordinary HTTPS requests to GitHub Pages, so there is nothing here for a blue team to flag beyond ordinary web traffic.
Limitations are the ones you would expect from a single-maintainer effort: no license file is declared in the repository metadata, which teams with strict dependency governance should factor in before institutionalizing it, and the curated keyword index on the site will always lag behind what a full local search can find. The search_keywords*.py naming with a wildcard suggests multiple script variants exist, so users should inspect the repository to pick the one matching their workflow. None of these caveats undercut the core utility.
For authorized threat intelligence analysts, malware researchers, and detection engineers, ThreatIntel-Reports is best understood as a search engine over the collective written memory of the threat intelligence community. Its combination of automatic collection, full-corpus local search via search_keywords*.py, JSON output for pipeline integration, and a browsable site with growing preset coverage of 2000+ tools and APT groups makes it a practical, low-friction addition to a defensive toolkit, and a good example of how aggregation sometimes beats sophistication.
mthcht/ThreatIntel-Reports.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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