IDA-Skill is an AI Agent skill package that lets an autonomous agent drive IDA Pro headlessly to triage malware samples like a human reverse engineer, intended for authorized research and defensive analysis.
| Tool | miunasu/IDA-Skill — an AI Agent skill that automates malware analysis in IDA Pro with LLM-assisted function understanding |
| Category | Malware analysis / reverse engineering automation with LLM agents |
| Primary Use | Automating triage of malicious binaries in an authorized lab: extracting imports, strings, decompiled code, C2 indicators, and YARA rules through an agent reading SKILL.md |
| Safe Use | For authorized malware analysts, SOC researchers, and defenders analyzing samples inside isolated lab environments with properly licensed IDA installations |
| Telemetry Note | The skill operates entirely against a local IDA database (idat64.exe), but REAI sends decompiled function code to a configured remote LLM API_URL — defenders should note that sample-derived code may leave the analysis network |
IDA-Skill from miunasu is a Python-based skill package, weighing in at 237 stars and licensed under AGPL-3.0 per the repository metadata, that turns an AI agent into something resembling a junior malware analyst operating IDA Pro. The premise is straightforward: instead of a human clicking through the disassembler, an agent reads the bundled SKILL.md, learns what tools are available, and then drives IDA headlessly to produce an analysis report. The README explicitly frames it as letting the AI analyze malicious samples "like a security analyst," which places it firmly in the growing category of agent-augmented reverse engineering tooling. What makes it interesting is not any single component but the composition: it stitches together a headless IDA driver, LLM-based function comprehension, and classic signature-based scanners into one agent-consumable package.
Architecturally, the skill follows the now-familiar pattern of agent skills: a declarative SKILL.md describing capabilities, plus a set of tools the agent can invoke. The headless execution hinges on config.json, where you point idat_path at your local idat64.exe binary — the text-mode incarnation of IDA Pro 9.0 that runs without the GUI. This is the same mechanism human automation has used for years via IDAPython batch scripts, but here the orchestration logic is delegated to the agent layer. The author demonstrates it with a separate multi-agent framework called Spore, also linked from the README, suggesting the skill is designed to be framework-agnostic as long as the host agent can read skill manifests and call the packaged tools.
The workflow the README describes is deliberately minimal from the operator's side: configure the IDA path, drop the skill into the agent's skill folder, and describe what you want in natural language. The agent then handles initialization of the IDA database, extraction of imports, strings, and decompiled code, and proceeds to recursive reverse analysis — identifying functionality, extracting C2 configuration, recognizing cryptographic algorithms, and finally generating a report. The explicit claim that you need no IDAPython knowledge is the key usability pitch: the agent reads the skill documentation itself and figures out which tool to call for which analytical question.
The flagship embedded tool is REAI, based on the author's other open-source project REAI_IDA. REAI pushes decompiled functions to an LLM and asks it to explain what the function does, with two concrete side effects that analysts will immediately recognize as valuable: automatic function renaming (sub_401000 becomes something like AI_decrypt_config) and automatic comments at call sites explaining what each sub-function does. Recursive call-chain analysis means the agent can walk a main routine outward, labeling everything it encounters. This is the same pattern seen in several LLM-assisted IDA plugins, but packaging it as an agent-invocable tool rather than an interactive plugin changes the interaction model entirely — batch triage rather than interactive assistance.
Configuration of REAI happens inside tools/reai.py, where you set API_KEY, API_URL, and MODEL. The README notes support for OpenAI, Azure, deepseek, and local models, which is a pragmatically important detail: analysts handling sensitive samples will want to point API_URL at a locally hosted model to keep decompiled code inside the lab boundary. Anyone deploying this in a real research environment should treat that configuration decision as a data-handling control, not a convenience option. Sending decompiled malware logic to a third-party API is a policy question most organizations will need to answer before first use.
The second embedded capability is FindCrypt, the well-known constant-based crypto identifier. It scans for signatures of AES, DES, RC4, Blowfish, and TEA, hashes including MD5, SHA1, and SHA256, and encodings like Base64 and CRC32. S-tables and other magic constants remain one of the cheapest high-signal detections in static analysis, and having the agent call this automatically during triage means encryption identification is no longer a manual early step. The third tool, Export Check, looks at DLL export tables for anomalies — oversized export functions and unusual export patterns — which is a heuristic for detecting functionality smuggled into otherwise innocuous-looking libraries, a common technique in sideloading chains.
On the extraction side, the README promises indicators including IP addresses, domains, file paths, and registry keys, plus YARA rule generation from opcode features. YARA generation is arguably the most operationally useful output for defenders: a triage run that ends with a usable rule closes the loop from one-time analysis to reusable detection. The README also lists persistence mechanism and process injection detection among the automated malicious-behavior identifications, though as with all LLM-driven behavioral claims, treat the outputs as hypotheses to verify rather than ground truth. An agent confidently labeling a function AI_connect_c2 is a lead, not a finding.
The licensing situation deserves attention before adoption. The README body repeatedly states the project is under GPL-3.0 and "for learning and research only, no commercial use," while the repository metadata records AGPL-3.0. Either way, the author's stated intent explicitly prohibits commercial use without separate authorization — an unusual restriction layered on top of a GPL-family license, and one that commercial CSIRTs and security vendors should resolve with the author before embedding this in production tooling. The disclaimer also reminds users to comply with applicable laws and software license agreements when performing reverse engineering, which is standard but worth heeding given IDA Pro itself carries strict licensing terms.
From a defensive perspective, where does this fit? The natural home is a malware triage pipeline inside an authorized lab: samples arrive, an agent runs the skill headlessly overnight, and analysts wake up to annotated databases, extracted configuration, candidate IOCs, and draft YARA rules. It lowers the floor for junior analysts while giving senior analysts a pre-labeled starting point. It is equally relevant to threat intelligence teams that need to process sample volume faster than human-only workflows allow, provided the LLM backend is chosen to match the sensitivity classification of the samples.
The telemetry and egress story cuts both ways. Because everything runs against a local IDA database via idat64.exe, the analysis artifacts stay on the analyst workstation — good for containment. But the REAI component makes outbound calls to whatever API_URL you configure, sending decompiled code off-box. Blue teams running this should monitor that egress path, prefer local model endpoints, and log which functions were submitted. In a research-network context, treating reai.py as the single network-touching component simplifies auditing considerably.
Limitations are implied rather than documented. The README is enthusiastic but thin on accuracy metrics — there is no evaluation of how often the LLM mislabels functions, which is the failure mode that matters most when agent-generated names start anchoring human conclusions. The demo video link requiring a manual movie.mp4 download suggests the project is early-stage and single-maintainer. Analysts adopting it should treat it as an accelerator for hypothesis generation, with verification — cross-checking FindCrypt hits, validating extracted C2 endpoints against sandbox behavior — remaining a human responsibility.
In sum, IDA-Skill is a competent assembly of existing ideas — headless IDA automation, LLM function annotation via REAI, FindCrypt signatures, export heuristics — into an agent-native package that reflects where malware analysis tooling is heading. For authorized analysts with a licensed IDA Pro 9.0 installation and a considered position on LLM data handling, it offers a genuinely useful automation layer; for everyone else, it is at minimum a clear preview of how agent skills will reshape reverse engineering workflows over the next few years.
miunasu/IDA-Skill.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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