// malware analysis
· from $20/moAn uncensored AI
for malware analysis.
Malware analysis is corpus-scale pattern matching where the marginal cost of a new sample should drop to zero once your pipeline is in place.
Senior analysts spend their best hours setting up that pipeline. Junior analysts spend their day triaging samples one at a time because the pipeline is not in place yet. AI assistance should fix that. Instead, the major coding assistants — Claude, ChatGPT, Cursor, Copilot — refuse to engage with malware samples on principle, treating defensive incident response and threat intelligence as if they were attacker workflows. The refusal is uniform and structural; you cannot rephrase your way past the policy. TartarusAI writes the triage harness, the YARA rule, the unpacker, the family classifier — without the moralizing about why a sample is malware. The agent treats malware analysis the way every commercial threat-intel platform already does: as a defensive discipline that does not need to justify itself to the tooling. Same enterprise-grade runtime safety guards. Content policy, off.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ triage these 200 samples — extract iocs, family-classify, write yara
target: incident response queue, sandboxed VM
▎▣ static-triaged 200 samples in 47s
▎+ wrote iocs.csv (847 IOCs across samples)
▎+ wrote family_clusters.json (5 distinct families)
▎+ wrote yara/cluster_a.yar, cluster_b.yar...
done.// what it does
What you ship
when nothing refuses.
Sample triage at scale
Static + behavioral analysis harnesses, IOC extraction (network, registry, mutex, file, persistence indicator), family clustering, capability scoring against MITRE ATT&CK. Run it across 10K samples and get back a clean dataset, not a folder. Output integrates with VirusTotal, MISP, TheHive, Cortex, GRR, Velociraptor.
Custom YARA + capa rules
Sample → distinguishing features → YARA rule that will not false-positive. Positive-set + negative-set tuning for precision/recall calibration. Capa-style capability extraction for behavior-based detection. Iterates against your corpus until the precision/recall trade-off is where you need it.
Packer / crypter unwrapping
Identify the layer, write the unpacker, dump the inner payload. Custom packers, custom crypters, custom string-obfuscation schemes — the agent writes the de-obfuscator without lecturing about the sample. Particularly strong on the cottage-industry packers in the long tail of malware families.
Threat-intel writeups
Sample analysis → blog-quality writeup. TTP mapping to MITRE ATT&CK, IOC tables, screenshot annotation suggestions, executive summary, technical deep-dive. Cuts the worst part of malware research — turning a week of analysis into something the rest of the team can read.
Detection engineering
YARA rule writing, Sigma rule generation for SIEM detection, EDR custom-rule contributions (CrowdStrike CQL, SentinelOne STAR, Defender KQL), Suricata / Snort signatures for network detection. The agent writes the rules from samples, you tune the precision against your false-positive budget.
Family classification + lineage tracking
Sample clustering by code reuse, by toolkit lineage, by author fingerprint. Useful for tracking actor evolution across campaigns and for de-duplicating triage work when the same family lands in your queue under different SHAs.
// workflow
IR triage at corpus scale
A sample lands in your queue. The traditional flow: open it in IDA / Ghidra, spend two hours figuring out what it is, write the YARA rule by hand, push to detection, move on. The TartarusAI flow: drop the sample (or the corpus) into the agent, get back static feature extraction + behavioral tags + family classification + draft YARA in minutes. You spend the saved hours on the samples that actually require deep human analysis.
For a real IR queue, the bigger win is the pipeline rather than the individual sample. The agent writes the triage harness once, then runs it across every new sample of that family without further human input. You get a clean dataset, the queue depth drops, and the senior analysts get their week back.
For threat-intel teams, the writeup is usually the bottleneck. You have done the analysis, you understand the sample, and now you need to turn it into something the rest of the org can act on. The agent ghostwrites the writeup from your annotated analysis notes — TTP mapping, IOC tables, screenshot annotation suggestions, executive summary, technical deep-dive. You review, edit, publish.
// integrations
Fitting into your existing IR + TI stack
TartarusAI does not replace VirusTotal, MISP, TheHive, Cortex, GRR, Velociraptor, OSQuery, or your custom SIEM. It writes the scripts that drive them. Custom MISP feed parsers, TheHive case templates, Cortex analysers, GRR / Velociraptor flows, OSQuery configurations, custom SIEM detection rules.
Outputs are raw scripts and structured data — JSON, CSV, YARA, Sigma. No SaaS lock-in, no proprietary format. You commit them to your detection-engineering repo and run them on your existing infrastructure. For Enterprise customers, on-prem deployment supports IR work where the sample data absolutely cannot leave the perimeter.
- ●Pairs with VirusTotal, MISP, TheHive, Cortex, GRR, Velociraptor, OSQuery, your custom SIEM.
- ●Generates YARA rules, Sigma rules, custom EDR detection (CrowdStrike CQL, SentinelOne STAR, Defender KQL), Suricata / Snort signatures.
- ●Outputs are raw rules and scripts you commit to your detection-engineering repo.
- ●Enterprise tier supports on-prem for sample data that cannot leave the perimeter.
// discipline
Why malware analysts need an uncensored model
Every IR analyst, every threat-intel researcher, every detection engineer has the same experience: they ask Claude or ChatGPT to help with a sample, the model refuses because the prompt mentioned "malware," and they switch back to the disassembler. The big assistants treat all malware-adjacent work as adversarial because the content policy cannot tell the difference between an analyst doing IR and a wannabe author writing their first dropper.
The cost of this conflation is paid by the defenders. Every minute a senior analyst spends arguing with the AI is a minute they are not analysing samples, writing detection rules, or training junior analysts. Every junior analyst stuck without senior-level pair-programming because the AI refuses is a junior analyst whose ramp is slower than it has to be.
TartarusAI removes the content layer entirely for malware analysis work. Sample triage, IOC extraction, YARA writing, packer unwrapping, family classification, threat-intel writeups — all in scope. The agent reads the disassembly, writes the analysis script, ports the unpacker, and treats the work as the defensive discipline it actually is. Runtime safety guards stay in place; they protect your project, not the model from your prompt.
// questions
What people actually ask.
Will it analyze live malware samples without refusing?+
Can it work with disassembly / decompilation directly?+
Does this fit into existing IR pipelines?+
What about anti-debug / anti-VM / sandbox detection?+
Can I use it on samples under embargo / NDA?+
Does it write Sigma rules / SIEM detections, not just YARA?+
How does it cluster by family vs author / actor?+
What about ransomware analysis specifically?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only