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AI-powered reverse engineering of compiled code to understand advanced malware, embedded risks, and supply chain risk. No source required.

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liblzma.so.5.6.0Supply Chain Attackconti_sampleMalware
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The fastest-growing risk is the software you cannot inspect

AI and abundant compute accelerate how software is built and modified. New logic enters compiled code faster than defenders can analyze, allowing harmful functionality to be embedded inside trusted software.

Reveal malware and hidden logic inside the software you trust with Delphos Labs

Delphos Labs uses AI-driven reasoning to analyze compiled code and expose malware, hidden logic, and underlying capabilities without execution, source, or signatures. Teams gain the visibility and confidence they need before software enters their environment.

mellow-argon-3e9c72afKittyKat.jpg | 738c486606d13093fe8eaba3d32d34a8bb835ccd1bc2b01572270b1cd5423dd4Binary AnalysisComponentsSecurity MitigationsFile DetailsNatural Language SummaryLikely Malicious Likely MaliciousVerdict:The majority of the code aligns with standard liblzma-like functionality, including streaming, header/footer processing, and selection. The suspicious aspects are the transient patch during resolution and the heavyweight initializer invoked through the . This in-process, one-shot, early-execution hook, which can , raises red flags for potential loader/instrumentation or malicious hooking. Lacking full context on the runtime callbacks and their provenance, the behavior is
suspicious but not conclusively malicious. The binary appears to be an implementation of framing functionality (), performing streaming encode/decode, header/footer processing, VLI encoding/decoding, and multi/single-threaded operation modes with a mature state-machine design. A notable, nonstandard technique is a transient GOT patch during IFUNC resolution that temporarily redirects a slot to a heavy in-memory initializer. This initializer sets up runtime structures and may tail-call into a runtime-provided callback, indicating a loader/instrumentation- style integration rather than a plain compression library startup. No evidence of network I/O, credential access, or destructive actions is observed; however, the load-time GOT patch is suspicious and warrants further investigation into the provenance of and the associated callbacks.CRCGOTIFUNCGOTtail-call into host-provided callbacksLZMA/XZliblzma-likedata_43c020 GOTIntegrates into existing workflows via the web app, API, and IDA Pro and Ghidra plugins.

Natural language summaries

Decision-ready summary reports that replace weeks of manual software analysis.

Detailed component manifests

A verified inventory of software components and dependencies that surfaces hidden weaknesses.

Insights into security mitigations

Actionable security analysis signals that help teams reduce organization-wide software risk.

Understand the Risk Across the Software You Build, Buy, and Run

Delphos Labs analyzes compiled software to detect threats, validate vendor applications, and identify components. No source code required. EDRs show runtime behavior, but Delphos Labs analyzes compiled code to reveal software's potential capabilities, both dormant and conditional logic.

Advanced Malware Analysis

Don't just detect malware, understand its capabilities. Expose stealthy, obfuscated malware and novel threats. Rapidly triage unknown files in minutes.

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Third-Party Software Risk

Understand the behavior and security risk of vendor software without source code. Expose hidden logic, and risky functionality for confident approvals.

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Scaled security analysis

Supply Chain Security

Ensure the integrity of software builds and updates. Detect tampering or unexpected changes between versions before shipping compromised software.

Actionable Insights
XZ Utils backdoor identified

True Visibility Into Opaque Software

Delphos Labs reveals the full potential and risk surface of every file. We make high-confidence, expert-level software assurance possible at scale. Automated, consistent, and defensible company-wide.

Agentic Reasoning

Proprietary, domain-specific models trained on millions of real-world files reason over compiled code like expert analysts. They surface patterns that generic LLMs miss.

Deep Insights

Reveals concealed logic, malicious payloads, and behavioral paths to show what software is actually capable of.

Efficiency & Scale

Delivers analyst-grade understanding 100x faster than traditional workflows, without additional headcount.

Explainability & Trust

Delivers clear, evidence-backed, plain-English verdicts and explanations of findings, why it matters, and how it was derived.

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