Software Composition Analysis (SCA) has become one of the central pillars of modern software security. As organizations increasingly depend on open-source components for speed, reliability, and innovation, SCA tools play a critical role in ensuring transparency, vulnerability management, license compliance, and supply-chain governance. While languages such as JavaScript, Python, Go, and Java benefit from centralized package managers and predictable dependency models, C and C++ remain uniquely difficult. The absence of a universal package ecosystem, broad variation in build systems, cross-platform constraints, and widespread code reuse create a dependency landscape radically different from higher-level languages.

In the context of secure development and regulatory requirements, particularly SBOM mandates, NIST and NIS2 alignment, and supply-chain risk frameworks, C/C++ SCA is not merely a technical challenge. It is a business, compliance, and operational necessity. If your product includes any significant C or C++ components: embedded systems, networking stacks, IoT firmware, robotics, automotive ECUs, medical devices, industrial control systems, cryptographic libraries, or security appliances and failing to understand your dependency graph is not an inconvenience; it is a systemic governance blind spot.

This article explores the deep technical realities of SCA for C/C++, the structural challenges that distinguish it from higher-level ecosystems, and the state of current solutions. Most importantly, it explains how Labrador Labs SCA platform was built with an understanding of the most demanding C/C++ environments, addresses the unsolved gaps: binary analysis, clone detection, variant tracking, coverage for vendor-patched forks, precise dependency reconstruction, and SBOM accuracy without guesswork.

Why SCA for C/C++ Is Fundamentally Different

Most SCA discussions assume a modern software ecosystem with standardized packaging metadata. But C/C++ predates these models by decades, and most of today’s software infrastructure, from Linux kernels to TLS libraries, wireless drivers to robotics runtimes, still depends heavily on C or C++.

Three factors make SCA for C/C++ categorically different:

No universal package manager
Widespread static linking, embedded reuse, and silent vendoring
Binary-level and clone-level analysis is required

Absence of a Universal Package System

JavaScript has npm. Python has pip. Java has Maven. Rust has Cargo. Go has modules.

C and C++ have:

CMake
Make
Autotools
Bazel
Ninja
QMake
Hand-written shell scripts
Manually copied headers
Vendor forks dropped into third_party/ directories

This diversity ruins the most straightforward assumption other SCA tools rely upon: dependency metadata is explicit, well-structured, and machine-readable.

In C/C++ projects, dependencies are often:

compiled directly into the project,
copied as source files (sometimes only the parts the vendor needed),
renamed or altered,
integrated via patches,
rebuilt with different compile options,
statically linked,
deeply nested inside firmware or ELF binaries.

SCA cannot rely on package.json-style metadata because it usually doesn’t exist. Instead, the tool must infer dependencies through pattern matching, AST analysis, control-flow and data-flow signatures, symbol extraction, or binary fingerprinting.

Binary-Level Analysis Is Mandatory, Not Optional

In C/C++, what appears in the source does not always reflect what ultimately ships.

Consider:

Static linking (e.g., OpenSSL statically embedded into a proprietary binary)
Link-time optimization that removes visible symbols
Compiled-out or inlined components
Firmware or appliance images where source is unavailable
Cross-compiled binaries with stripped symbols

This means effective SCA for C/C++ must operate at both levels:

Source analysis, when source is present
Binary analysis, for completeness

Binary SCA requires capabilities that simply don’t exist in higher-level ecosystems:

Extracting symbols (ELF, Mach-O, PE)
Reconstructing transitive relationships
Fingerprinting library functions across optimizations
Identifying vendor-patched forks

This is a non-trivial problem even for well-known libraries like zlib, libpng, libcurl, OpenSSL, mbedTLS, or SQLite, libraries that appear in thousands of products, frequently modified and almost never declared.

Code Clones, Borrowed Implementations, and Vendor Forks

C/C++ developers still frequently:

Copy code from open-source repositories into internal codebases
Implement partial features from well-known libraries
Maintain long-lived internal forks
Patch upstream components without documenting the changes
Backport security fixes instead of upgrading

This produces what SCA engineers call “non-verbatim clones.“

Traditional SCA tools fail here because they rely on filename or manifest matching, useless when files are renamed, relocated, split, or modified.

An accurate C/C++ SCA engine must detect these clones even if:

filenames changed,
directory structures changed,
implementation style changed,
comments were stripped,
code was reorganized,
only specific modules of a large library were used.

This requires deep similarity analysis at the structural, semantic, and control-flow level and not simple pattern matching.

Key Aspects of C/C++ SCA

Below we expand upon the major pillars of SCA, vulnerability detection, license compliance, dependency mapping, and SBOM generation, specifically in the context of C/C++.

Vulnerability Detection for C/C++

Vulnerability detection in C/C++ is materially different because:

Libraries may not be declared, so identification comes first.
CVEs may refer to functions or code constructs rather than versions.
Vendors frequently patch vulnerabilities without updating the version number.
Forks may contain vulnerabilities not present in upstream.

Identifying Known Vulnerabilities (CVE Matching)

CVE databases focus on version numbers. But in C/C++, you cannot rely on:

metadata,
dependency manifests,
package managers.

Therefore, the tool must infer version equivalence by:

fingerprinting files,
identifying unique code signatures,
matching commit-level patterns,
analyzing symbol exports,
spotting vulnerable function call sequences.

Detecting Developer-Patched Fixes

In C/C++, developers often cherry-pick patches, especially for:

OpenSSL,
zlib,
libjpeg,
SQLite,
glibc,
BusyBox.

This means:

The source code may technically be “old,” but hardened.
The source code may be “new,” but still missing a critical fix.

An SCA solution must go beyond version checking and detect:

specific vulnerable lines,
specific patched patterns,
vulnerable code clones inside vendor forks.

Vulnerable Clone Detection

SCA for C/C++ must detect:

direct clones,
near-miss clones,
inlined functions,
partial implementations,
ABI-modified forks.

This is where traditional SCA fails badly. Without semantic clone detection, vulnerability coverage collapses.

Fix Recommendations

Once vulnerabilities are detected, an SCA engine should:

recommend updates,
provide patch references,
identify commit-level fixes,
highlight breaking changes,
surface upgrade implications.

Modern organizations expect actionable remediation, not merely detection.

License Compliance in C/C++

License compliance in C/C++ requires extremely thorough identification of:

file-level licenses,
per-module licensing,
transitive licensing,
SPDX identifiers,
embedded attribution blocks.

C/C++ projects frequently mix:

Apache-licensed code,
GPL components,
BSD/MIT fragments,
vendor-specific licenses,
historical licenses (e.g., OpenSSL license pre-SPDX era).

These often coexist within the same binary.

License Complexity Factors

Key challenges include:

Code copies with missing license headers
Merged libraries sharing a single LICENSE file
Projects combining proprietary and GPL code
Embedded firmware without source access
Dual-licensed libraries (GPL + commercial)

A mature SCA tool must detect:

all license obligations,
attribution requirements,
conditions on redistribution,
copyleft implications,
crypto export considerations for some libraries.

Automated Compliance Alerts

Organizations need:

warnings for copyleft contamination,
alerts for missing attributions,
flags for incompatible license combinations,
export restriction warnings.

Simple license identification is not enough, context matters.

Dependency Mapping and Clone-Aware Graph Construction

Dependency mapping in C/C++ must reconstruct:

direct dependencies,
transitive dependencies,
static libs,
vendored source trees,
partial libraries,
reused snippets.

Unlike ecosystems with explicit manifests, C/C++ dependency graphs must be discovered.

An SCA tool must detect:

reused implementations of small libraries,
protocol stacks (e.g., mbedTLS modules),
crypto primitives reused across vendor code,
POSIX compatibility layers,
cut-down open-source modules included in closed appliances.

Dependency mapping must not break under:

directory renaming,
code splitting,
symbol stripping,
dead-code elimination,
inlining optimizations.

SBOM Generation for C/C++

For compliance (FDA, ENISA, NTIA, NIST, DoD, NIS2, CRA), organizations require accurate SBOMs.

Formats

Common SBOM formats include:

SPDX
CycloneDX
SWID

A C/C++ SBOM must also include:

embedded libraries,
inlined components,
statically linked dependencies,
vendor forks,
build tool versions,
transitive analysis.

Completeness Requirements

For C/C++ SBOMs to be meaningful, they must include:

exact versions or commit hashes,
file-level mappings,
binary-level fingerprints,
license disclosures,
known vulnerabilities,
dependency relationships.

Incomplete or inaccurate SBOMs create liability, not protection.

How Labrador Labs’ SCA Solution Solves the Hard Problems of C/C++

Labrador Labs was built on the assumption that existing SCA products fail precisely where C/C++ complexity begins. Its architecture is designed to operate in real-world environments:

firmware images,
embedded devices,
medical devices,
automotive ECUs,
ICS systems,
proprietary appliances,
networking products,
cross-compiled toolchains.

Core Technical Advantages of Labrador Labs for C/C++

Binary-First SCA Architecture

Rather than starting with source code, Labrador Labs begins with:

binaries,
firmware,
archives,
static/dynamic libraries,
stripped executables.

It fingerprint the actual code that ships, not just what developers believe is present.

This ensures:

accurate dependency detection,
no reliance on developer metadata,
robust coverage for embedded systems,
identification of statically linked libraries.

Deep Code Clone Detection

Labrador Labs implements semantic analysis techniques capable of identifying:

code that has been modified,
code that has been refactored,
partial implementations of libraries,
inlined or optimized functions,
backported patches,
non-verbatim clones.

This is essential for:

crypto libraries,
compression libraries,
network stacks,
TLS implementations,
embedded protocol handlers.

Vulnerability Detection That Goes Beyond Version Numbers

Instead of relying solely on version metadata, Labrador Labs:

examines vulnerable functions directly,
detects whether affected lines exist,
checks whether patches are present,
identifies backported fixes,
verifies variant differences,
matches commit-level patterns.

This is crucial for libraries like:

OpenSSL,
mbedTLS,
zlib,
libcurl,
glibc,
SQLite.

Detection of Vendor Forks

Because Labrador Labs detects structural similarities and differences, it can:

identify forks,
determine divergence points,
understand missing fixes,
detect latent vulnerabilities.

This reduces the false sense of security often associated with legacy forks.

License Compliance With Precision

Labrador Labs performs:

file-level license detection,
header signature extraction,
SPDX reconstruction,
analysis of missing notices,
detection of proprietary vs open-source boundaries.

It highlights:

copyleft contamination,
missing attributions,
dual-license implications,
redistribution obligations.

This is critical for industries with strict licensing requirements, including automotive, medical device manufacturers, telecommunications, and embedded systems vendors.

Accurate SBOM Generation for C/C++ Ecosystems

Labrador Labs produces SBOMs that:

identify dependencies at the binary level,
detect modified components,
assign correct versions even for partial clones,
include transitive dependencies,
comply with SPDX, CycloneDX, SWID.

This results in SBOMs that:

pass regulatory audits,
satisfy FDA (Premarket SBOM requirements),
align with ENISA and NIS2 transparency expectations,
support U.S. Executive Order 14028,
meet CRA (Cyber Resilience Act) obligations.

Unlike other tools, Labrador Labs can generate SBOMs without source code, making it uniquely suited for:

third-party components,
acquired firmware,
legacy systems,
OEM-supplied binaries.

Supply Chain Accuracy and Governance

Labrador Labs also supports:

risk scoring,
license governance workflows,
issue prioritization,
remediation guidance,
SLA reporting,
continuous monitoring.

This transforms SCA from a developer task into a governance process aligned with:

regulatory frameworks,
procurement audits,
security operation workflows,
enterprise risk dashboards.

Practical Use Cases for C/C++ SCA

Embedded Systems & Firmware

Most embedded devices rely heavily on C/C++ libraries, often statically linked and undocumented. Labrador Labs automatically identifies:

network stacks,
TLS libraries,
compression libraries,
cryptographic implementations,
filesystem libraries.

This is especially important for industries such as:

medical devices,
automotive,
consumer IoT,
industrial controls.

Security Appliances and Networking Products

Products like VPN appliances, firewalls, routers, and Wi-Fi controllers rely on:

OpenSSL,
Hostapd,
Dnsmasq,
BusyBox,
iptables.

Labrador Labs can fingerprint these components even in stripped binaries.

High-Security Environments

Military, national security, and critical infrastructure projects require precise tracking of:

provenance,
vendor forks,
backported fixes,
compliance documentation.

Labrador Labs produces audit-ready SBOMs that reflect reality.

Integration Into the SDLC and CI/CD Pipelines

Labrador Labs is built to integrate at multiple layers:

pre-commit scanning,
CI/CD pipeline integration,
nightly analyzes,
release-level SBOM generation,
post-build binary scanning.

For C/C++, integration focuses on:

build artifacts,
object files,
compiled dependencies,
cross-compiled outputs,
firmware images.

This allows discovery of dependencies invisible at the source level.

Why Traditional SCA Fails for C/C++ and Labrador Labs Succeeds

Challenge	Typical SCA Tools	Labrador Labs
No package metadata	Fail or partial detection	Binary + clone analysis
Vendor forks	Missed or misidentified	Detected at structural level
Backported fixes	Impossible to detect	Patch pattern detection
Modified open-source code	Treated as proprietary	Clone identification
Stripped binaries	Dependency blind spots	Symbol and fingerprint matching
SBOM accuracy	Approximate guesses	True dependency reconstruction
Firmware scanning	Limited or unsupported	Native capability
Transitive analysis	Manifest-limited	Binary-level reconstruction

Labrador Labs solves the real problems inherent in C/C++.

Software Composition Analysis for C/C++ is fundamentally different from SCA for higher-level languages because:

dependencies are not declared,
binaries are often static and opaque,
code reuse happens through copying, not packaging,
vendor forks are ubiquitous,
patches are unevenly applied,
build systems vary widely,
source code alone does not reveal the complete picture.

This complexity makes C/C++ SCA one of the most challenging problems in the software security and compliance ecosystem.

Existing SCA tools provide partial solutions. They excel in environments with rich metadata and predictable dependency relationships, but they struggle with:

binary analysis,
clone detection,
fork identification,
backported patch detection,
accurate SBOM generation for embedded systems.

Labrador Labs addresses all these challenges head-on with an architecture built specifically for industries where C/C++ dominance is non-negotiable – medical, automotive, aerospace, embedded, IoT, telecom, defense, and critical infrastructure.

Its combination of binary-first scanning, semantic clone detection, patch-aware vulnerability analysis, and compliance-grade SBOM generation positions it as a next-generation SCA platform capable of meeting both technical and regulatory requirements.

For organizations that depend on C/C++ – and therefore depend on the security of their open-source software supply chain – Labrador Labs provides accuracy, completeness, and trustworthiness unmatched by traditional SCA tools.

Software Composition Analysis (SCA) for C/C++