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SOURCE CODE REVIEW · SAST · MANUAL ANALYSIS

Find Vulnerabilities Early.Enforce Secure Coding.Ship With Confidence.

Enterprise SAST combined with expert manual review. We detect injection flaws, auth bypass, business logic errors, and OWASP Top 10 across 10+ languages — integrating directly into your CI/CD pipeline with zero friction.

Audit Methodology · Threat-Model Driven · 5-Phase Protocol

The Audit Blueprint

A precision-engineered 5-phase review protocol — from attack surface enumeration to verified remediation sign-off. Every engagement follows the same rigorous playbook.

01
Day 1 · 6h
Lead Architect + Security Engineer
Scope Definition
Threat model, attack surface map, DFD
31
Attack surface entry points mapped
Scope definition prevents 67% of review time wasted on low-impact components
Inputs
Codebase manifest
Architecture docs
Data flow diagram
Client brief
Outputs
STRIDE threat model
Attack surface map
Tier classification
Risk register v0
spakto-audit · phase-01 · scopeRUNNING
$ threat-dragon --model stride_model.json --export
[✓] 14 threat actors identified
[✓] 6 trust boundaries mapped
[✓] 31 attack surface entry points
[!] 3 HIGH-RISK data flows — flagged for deep review
$ dread-score --assets assets.yaml --output risk_register_v0.csv
Scored 28 assets · Top risk: AuthService (8.4/10)
$ echo "Scope locked — review depth: Tier-1 x8, Tier-2 x14"
execution progressphase 1/5
Toolchain
OWASP Threat Dragon
STRIDE threat modelling
draw.io DFD
Data flow diagramming
MITRE ATT&CK
TTP mapping
DREAD matrix
Risk pre-scoring
Engagement Flow
5
Review Phases
Scope → Verified fix
40K+
Auto Rules Run
SAST · SCA · Secrets · IaC
CVSS 3.1
Risk Standard
+ EPSS exploit probability
30-day
Retest SLA
Verified remediation cert
Shift-Left Security · Commit-to-Deploy Pipeline · Zero False-Positive GatesPIPELINE v4.2 · ACTIVE

SAST & Automated Analysis Pipeline

Every commit triggers a multi-layer security pipeline — static analysis, dependency scanning, secret detection, and policy enforcement — all in under 4 minutes with zero developer friction.

Live Pipeline Execution — PR #2847 · main ← feature/user-auth
Git Commit✓ 2.1sSAST Scan✓ 48sSCA✓ 31sSecrets✓ 12sPolicy⟳ RunningDeployPending
2.1s
Git Hooks
✓ PASS
48s
SAST Analysis
✓ PASS
31s
SCA / Deps
✓ PASS
12s
Secrets Scan
✓ PASS
~
Policy Gate
⟳ RUNNING
PR Merge
— QUEUED
Pipeline Stage Breakdown
Per-stage configuration & enforcement policy
🔍
SAST Analysis
Semgrep + Bandit + ESLint
~48sGATE
1,247 custom + community rules
AST-level taint tracking across call graphs
Inter-procedural data flow analysis
Framework-specific rule overlays (Django, Spring, Express)
Fail on CRITICAL or HIGH ≥ 3
📦
SCA / Dependencies
OWASP Dep-Check + Snyk
~31sGATE
698,000+ known CVE database (NVD + GitHub Advisory)
License compliance scanning (GPL, AGPL detection)
Transitive dependency analysis (12 levels deep)
SBOM generation (SPDX + CycloneDX)
Fail on CVSS ≥ 9.0
🔐
Secrets Detection
TruffleHog v3 + Gitleaks
~12sGATE
Entropy analysis across all commit history
120+ detector patterns (AWS, GCP, Stripe, Slack…)
Regex + semantic pattern matching
Pre-commit hook + CI gate (dual coverage)
Fail on any confirmed secret
⚖️
Policy Gate
Spakto Policy Engine
~4sGATE
Configurable severity thresholds per repo
Exception workflow with CISO approval flow
Trend gating: fail if findings increased vs last scan
SLA auto-assignment for each finding
Configurable per team/repo
GitHub Actions · security-scan.yml
● IN PROGRESS
$ semgrep scan --config=spakto --severity=ERROR,WARNING
[INFO] Loading 1,247 rules from spakto registry...
[INFO] Scanning 3,847 files across 12 directories
─────────────────────────────────────────────────────
WARN src/api/UserController.py:42
sql-injection: Raw SQL query with user input (CWE-89)
q = "SELECT * FROM users WHERE id=" + user_id
WARN src/auth/session.py:89
missing-auth: Endpoint missing authentication check (CWE-306)
─────────────────────────────────────────────────────
$ owasp-dependency-check --project spakto-app
[INFO] Checking 247 dependencies against NVD + GitHub Advisory
[CRIT] CVE-2024-21733 in django==3.2.14 (CVSS 9.8)
[HIGH] CVE-2024-45401 in pillow==9.3.0 (CVSS 7.5)
─────────────────────────────────────────────────────
$ trufflehog git --since-commit HEAD~1 --only-verified
[PASS] No verified secrets detected in diff
[INFO] 23 entropy candidates — all false positives filtered
─────────────────────────────────────────────────────
═══ POLICY GATE EVALUATION ═══
[FAIL] SAST: 1 CRITICAL finding — build blocked
[FAIL] SCA: 1 CRITICAL CVE (CVSS 9.8) — build blocked
[INFO] Full report: https://spakto.io/reports/pr-2847
═══ PR MERGE BLOCKED — Fix 2 issues to proceed ═══
$
Total scan time: 1m 37s · 3,847 files · 247 deps2 blocking issues found
Integrated Tool Matrix
Every tool configured, tuned, and maintained by Spakto engineers
ToolCategoryLanguagesRulesSpeedOutput
SemgrepSAST15+1,247< 60sSARIF, JSON, PR comments
BanditSASTPython47< 10sJSON, HTML, XML
ESLint SecuritySASTJS/TS28< 15sJSON, JUnit
OWASP Dep-CheckSCAAllCVE DB< 30sHTML, XML, SARIF
SnykSCAAll1.3M+< 45sJSON, SARIF
TruffleHog v3SecretsAll120+< 15sJSON, verified
GitleaksSecretsAll160+< 8sJSON, SARIF
CheckovIaCTF/K8s750+< 20sJSON, JUnit
GitHub Actions
✓ Native
GitLab CI
✓ Native
Bitbucket Pipelines
✓ Native
Jenkins
✓ Native
CircleCI
✓ Native
Azure DevOps
✓ Native
AWS CodePipeline
✓ Native
Tekton
✓ Native
OWASP Top 10 2021 · CWE/SANS Coverage · Real-Time Detection699+ CWE MAPPED

OWASP Top 10 & CWE Coverage

Full coverage of OWASP 2021 with 699+ CWE mappings — every category includes context-aware detection, exploit-path tracing, and developer-readable remediation guidance.

A01:2021CRITICAL

Broken Access Control

34
CWEs mapped

Access control enforces policy so users cannot act outside intended permissions. Failures lead to unauthorized disclosure, modification, or destruction of data, and performing business functions outside user limits.

Key CWE Mappings
CWE-862CWE-863CWE-639CWE-284CWE-285+29 more
Spakto Detection Rate88%
94%
Prevalence
of apps tested
3.81%
Incidence
avg rate
#1
Category Rank
OWASP 2021
spakto · a01_2021_detection.py
● LIVE SCAN
01def get_invoice(invoice_id):
02 invoice = Invoice.get(id=invoice_id)
[CRITICAL] IDOR — no ownership check · CWE-639
03 return invoice.data # any user gets any invoice
04
05@app.route("/admin/users")
[HIGH] Missing role check · CWE-862
06def admin_users(): # no @require_admin decorator
Semgrep + custom rules · AST-aware · inter-proceduralDetection: 88% confidence
Human-Driven Analysis · Beyond Rule-Based DetectionEXPERT REVIEW PROTOCOL v2.1

Expert Manual Review Process

Senior security engineers trace business logic, map trust boundaries, and reason about adversarial intent — finding vulnerabilities that no automated scanner can detect.

5-Phase Expert Review Workflow
Typical 5-day deep-dive engagement timeline
Review Path
Findings
01
Scope & Threat Model
Day 1–2
Asset inventory
Attack surface map
Adversary profiling
Scope definition
024
Architecture Analysis
Day 2–3
Trust boundary review
Component isolation
Data flow tracing
3rd-party risk
037
Business Logic Review
Day 3–4
Workflow abuse
IDOR hunting
Privilege escalation
Race condition testing
045
Auth & Cryptography
Day 4–5
Session analysis
Token validation
Crypto parameter audit
Key management
0516
Report & Remediation
Day 5+
CVSS scoring
PoC writing
Fix guidance
Retest scheduling
Detection Coverage Comparison
Manual review vs automated SAST — what each finds
💉SQL / Command Injection
SAST Auto95%
Manual98%
🔓Business Logic IDOR
SAST Auto8%
Manual91%
Race Conditions (TOCTOU)
SAST Auto6%
Manual84%
🔐Auth Workflow Bypass
SAST Auto4%
Manual89%
🔑Subtle Crypto Misuse
SAST Auto28%
Manual94%
🏗️Architecture Trust Flaws
SAST Auto2%
Manual87%
🔄Second-Order Injection
SAST Auto12%
Manual78%
analyst · PaymentController.py
● REVIEWING
01def process_order(user_id, order_id, qty):
02 order = Order.objects.get(id=order_id)
IDOR

No ownership check — any user can access any order via order_id

03 user = User.objects.get(id=user_id)
04 if user.balance >= order.total:
RACE CONDITION

Balance check and deduct not atomic — TOCTOU window exploitable

05 user.balance -= order.total * qty
06 order.status = "processed"
07 notify_warehouse(order_id, qty)
BUSINESS LOGIC

qty not validated — negative qty reverses payment, credits attacker

08 return {"ok": True}
09 raise InsufficientFunds()
3 findings · 0 auto-detectable by SAST · Analyst: SR-07Day 3 of 5
🏗️
SAST miss: 98%4 findings

Architecture & Trust Boundaries

Maps component isolation, data flows across trust boundaries, and identifies where the threat model breaks down.

Lateral privilege propagation
Unsafe inter-service trust
Unvalidated internal APIs
Dependency chain abuse
🔓
SAST miss: 92%7 findings

Business Logic & IDOR

Traces multi-step workflows to find where authorization checks are skipped, reordered, or bypassed via workflow manipulation.

Object reference enumeration
Step-skipping abuse
Payment reversal logic
Role assumption flaws
SAST miss: 94%3 findings

Race Conditions & TOCTOU

Identifies timing windows between check and use — concurrency flaws requiring understanding of async execution paths.

Balance double-spend
Session fixation via timing
File lock bypass
Distributed cache races
🔐
SAST miss: 60%5 findings

Auth & Session Management

Reviews token generation, expiry, rotation logic, and session lifecycle — including subtle edge cases in OAuth/OIDC flows.

JWT algorithm confusion
Session fixation post-login
Refresh token leakage
Scope creep in OAuth
🔑
SAST miss: 72%3 findings

Cryptography Misuse

Beyond "use AES-256": validates IV reuse, key derivation functions, entropy sources, and protocol-level weaknesses.

CBC padding oracle
ECB mode detection
Predictable IV/nonce
HMAC key confusion
🔄
SAST miss: 88%4 findings

Second-Order & Stored Attacks

Tracks data that enters a safe store but surfaces unsanitized later — requiring end-to-end tracing across storage layers.

Stored XSS via import/export
Second-order SQL injection
Template injection at render
Serialization gadget chains
Polyglot Static Analysis · 10 Languages · Framework-Aware DetectionRULE ENGINE v3.8

Multi-Language Analysis Engine

Framework-aware AST parsing, inter-procedural taint tracking, and language-specific rule sets — 9,000+ detection signatures across 10 languages, updated weekly.

Python · Detection Coverage
Django · Flask · FastAPI · SQLAlchemy · Celery
1,247 rules
CWE-89SQL / ORM Injection
98%
847 detection rules
CWE-78Command Injection
96%
203 detection rules
CWE-94Jinja2 SSTI
91%
89 detection rules
CWE-502Pickle Deserialization
89%
156 detection rules
CWE-22Path Traversal
94%
312 detection rules
Framework Coverage
DjangoFlaskFastAPISQLAlchemyCeleryaiohttp
spakto-sast · python_scan · live● SCANNING
01
def get_user(user_id: str):
02
q = "SELECT * FROM users WHERE id=" + user_id[CRITICAL] SQL Injection · CWE-89
03
return db.execute(q).fetchone()
04
05
def render(tmpl_name, ctx): # user-controlled
06
t = env.get_template(tmpl_name)[HIGH] SSTI · CWE-94
07
return t.render(**ctx)
AST depth: 12 · Taint paths: 47 · Call graph: 2,341 nodes5 categories scanned
Detection Coverage Matrix
Vulnerability category coverage % per language · click a language pill above to highlight
LanguageInjectionMemoryCryptoAuthDeserial.XSS/CSRFRace
Python
98%
40%
85%
90%
89%
92%
60%
Java
95%
55%
88%
85%
97%
91%
72%
Node.js
92%
30%
82%
88%
78%
94%
75%
Go
94%
60%
86%
83%
65%
80%
88%
C/C++
75%
97%
78%
70%
72%
65%
85%
PHP
97%
35%
80%
88%
91%
96%
50%
Ruby
95%
40%
82%
87%
93%
89%
55%
.NET
94%
50%
87%
86%
96%
90%
68%
Rust
88%
85%
84%
78%
60%
72%
82%
Swift
80%
75%
95%
85%
70%
78%
45%
Composite Risk Scoring · Multi-Signal Vulnerability IntelligenceRISK ENGINE v2.4

Security Debt Scoring & Risk Prioritization

Five-signal composite scoring: CVSS severity, EPSS exploit probability, business asset criticality, code reachability, and vulnerability age — unified into a single actionable risk score.

Overall Security Posture
Computed across 116 active findings
● LIVE
67out of 100NEEDS IMPROVEMENT
Critical Findings3
High Risk12
Overdue SLAs7
Unpatched Deps23
Clean Components41
Composite Score Formula
Weighted five-signal risk calculation
// Composite Risk Score ∈ [0, 10]
R = 0.35·CVSS + 0.20·EPSS
+ 0.25·BizImpact + 0.15·Reach + 0.05·Age
// All signals normalized to [0–10] before weighting
35%CVSS Base Score
8.2
Attack complexity · scope · CIA impact
20%EPSS Probability
6.7
Exploit prediction (30-day window)
25%Business Impact
9.1
Asset criticality × data sensitivity tier
15%Code Reachability
7.4
Static + dynamic call graph analysis
5%Vulnerability Age
5.0
Days open × SLA breach multiplier
Active Findings Risk Register
CRITICALHIGHMEDIUM
IDComponentCVSSEPSS %Biz ImpactAgeCompositeSLA
SCR-001AuthController.java9.878.3%
9.5
14d
9.4
BREACH
SCR-007PaymentService.py8.152.1%
9.8
8d
8.9
3 days
SCR-012UserAPI/routes.ts7.531.4%
7.2
21d
7.3
BREACH
SCR-019FileUpload.php8.818.7%
6.1
5d
7.1
12 days
SCR-031ReportGen.go5.39.2%
7.8
45d
6.2
BREACH

CI/CD Security Gate Integration

Automated security checks with PR feedback, build gates, and developer-friendly actionable findings.

Build Gates

  • Fail on Critical/High
  • Configurable thresholds
  • Trend analysis
  • Exception workflows

PR Comments

  • Inline vulnerability markers
  • Auto-remediation suggestions
  • CWE/CVSS references
  • Blame awareness

Reporting

  • Dashboard metrics
  • Trend reports
  • SLA tracking
  • Compliance export

Sample PR Check Output

✓ Code Review Check
src/api/user.js
Line 42: SQL Injection [CRITICAL]
query = "SELECT * FROM users WHERE id = " + userId
Fix: Use parameterized queries (CWE-89)
Line 156: Weak Crypto [MEDIUM]
crypto.createCipher('aes192', password)
Fix: Use createCipheriv instead (CWE-326)
AI-Assisted Analysis · Beyond Rule-Based ScanningSAST++ ENGINE v4.1

AI-Assisted Secure Code Analysis

Context-aware static analysis that understands data flow, auth logic, and business intent — not just pattern matching. Senior security engineer-level reasoning at machine speed.

spakto-sast++ · analysis/UserController.py
001def transfer_funds(user_id, amount, target_account):
002 user = db.query(f"SELECT * FROM users WHERE id={user_id}")[CRITICAL] SQL Injection T1190
003 if user.balance >= amount:
004 # No CSRF token validation[HIGH] CSRF · CWE-352
005 target = get_account(target_account)
006 user.balance -= amount
007 # Missing authorization: any user can transfer![CRITICAL] Auth Bypass · CWE-862
008 target.balance += amount
009 log_transaction(user_id, amount)
010 return {"status": "success"}
AI Reasoning Engine
→ Data flow trace: user_id (HTTP param) → SQL query → UNPARAMETERIZED · CVE class: CWE-89
→ Auth gap: transfer_funds() reachable without ownership check on user_id → privilege escalation
→ Business logic flaw: attacker transfers FROM any account with known user_id
Deep Data Flow Tracking

Traces tainted input from HTTP layer through function calls to sinks (SQL, shell, file, DOM). Understands aliases, wrappers, and ORM abstractions.

Input → Output trackingInter-procedural analysisORM-aware taint propagation
Context-Aware Rule Engine

Goes beyond regex patterns — understands business context, auth boundaries, and access control intent. Detects logic errors that rule-based scanners cannot see.

Business logic flaw detectionAuth boundary analysisSemantic code understanding
🛡
OWASP Top 10 + CWE 699+

Complete coverage of injection, broken auth, cryptographic failures, IDOR, SSRF, XXE, and 699+ CWE categories with context-aware exploitation assessment.

OWASP Top 10 (2021)CWE/SANS Top 25Custom business rule checks
Shift-Left · Every Commit · Every PR · Every Build

Continuous DevSecOps Scanning

Security runs alongside development — not after it. Every commit triggers an automated security gate that blocks vulnerable code before it reaches production.

Live Pipeline · PR #2847 — feature/auth-refactor
Git PushDONE00:00s
feat: refactor auth token handling
SAST ScanDONE00:47s
2 CRITICAL · 5 HIGH · 12 MEDIUM detected
Secrets ScanDONE01:02s
1 AWS key exposed in .env.example
!
SCA / DepsDONE01:18s
CVE-2024-21733 in spring-boot 3.1.4
🚫
Policy GateBLOCKING01:19s
BLOCKED — Critical severity threshold exceeded
PR MergePENDINGs
Waiting for security remediation
Platform Integrations
GitHub Actions
GitLab CI
Bitbucket
Jenkins
CircleCI
Azure DevOps
ArgoCD
Tekton
Security Gate Config
# .spakto-security.yml
gate:
block_on_critical: true
block_on_high: true
secrets_scan: enabled
sca_scan: enabled
notify: slack, email
< 90s
Avg Scan Time
100%
PRs Gated
0
Critical in Prod
4.2s
Feedback Latency
Stop Leaks Before Attackers Find Them● LIVE SCANNING

Secret & Credential Detection

Detect API keys, tokens, passwords, and certificates exposed in source code — including full git history. Auto-revoke, alert, and rotate before attackers exploit the window.

gitleaks scan · repo: backend-api · 4,821 commits
AWS Access KeyCRITICAL23 commits ago
.env.example:14 · commit a3f8c1d
AKIA4XYZ...redacted
PostgreSQL PasswordCRITICAL47 commits ago
config/database.py:8 · commit b91e2f4
pgpass=Sup3rS3cr3t!
Stripe Live KeyCRITICAL3 commits ago
src/payment/stripe.js:3 · commit c120d8e
sk_live_...redacted
GitHub PATHIGH12 commits ago
tests/fixtures.yaml:92 · commit d7a4b2c
ghp_...redacted
Docker Hub TokenHIGH8 commits ago
Dockerfile:22 · commit e55f1a9
dckr_pat_...redacted
5 secrets found · Auto-revoke initiated · Alert sent to security@company.com
Secret Type Coverage
AWS / GCP / Azure Keys
GitHub / GitLab Tokens
Stripe / PayPal Keys
Database Passwords
JWT / OAuth Secrets
Private Keys (RSA/EC)
Slack / Telegram Webhooks
Docker / NPM Tokens
SSH Private Keys
SendGrid / Twilio API
Kubernetes Secrets
Env File Credentials
Auto-Response Workflow
1
Detect
Regex + entropy analysis + ML classifier scans all commits
2
Validate
Verify credential is live (not already rotated)
3
Alert
PagerDuty + Slack + email within 30 seconds
4
Revoke
Auto-revoke via cloud provider APIs (AWS, GCP, Stripe…)
5
Report
Full incident report with exposure window and blast radius
< 30s
Alert Time
100%
History Scanned
12
Secret Types
Your Weakest Dependency Won't Break You

Dependency & Supply Chain Security

Software Composition Analysis that maps your full dependency tree, identifies CVEs, tracks exploit availability, and generates SBOM for compliance. XZ Utils won't happen to you.

Dependency Risk Tree · backend/requirements.txt
django==4.2.7LOW
├── sqlparse==0.4.3CVE-2024-4340HIGH
├── asgiref==3.7.2LOW
cryptography==41.0.3CVE-2024-26130CRITICAL
├── cffi==1.16.0LOW
requests==2.28.1CVE-2023-32681HIGH
├── urllib3==1.26.14CVE-2024-37891HIGH
└── certifi==2022.12.7CVE-2023-37920MEDIUM
pyjwt==2.4.0CVE-2022-29217CRITICAL
pillow==9.3.0CVE-2023-44271HIGH
2
CRITICAL
4
HIGH
1
MEDIUM
3
CLEAN
CVE Intelligence

Real-time NVD/OSV/GitHub Advisory feed. EPSS scores show actual exploit probability. Patch available tracking with auto-upgrade PRs.

NVD IntegrationEPSS ScoringExploit PoC DetectionAuto-Upgrade PRs
SBOM Generation

Produce CycloneDX or SPDX SBOMs for regulatory compliance (EO 14028, EU CRA). Full transitive dependency coverage across 20+ package ecosystems.

CycloneDX 1.5SPDX 2.3EO 14028 ReadyEU CRA Compliant
Supply Chain Attack Detection

Detect typosquatted packages, dependency confusion attacks, malicious maintainer takeovers, and XZ Utils-style injected backdoors in CI.

TyposquattingDep. ConfusionMaintainer TakeoverBackdoor Detection
We Follow Data Like Attackers Do

Code Flow & Data Flow Analysis

Track every data path from user input to sink — SQL queries, shell commands, file ops, HTTP calls, DOM writes. Find injection vulnerabilities that flow analysis misses at rule level.

Taint Flow Graph · POST /api/search
HTTP POSTquery paramTAINTEDsearch_view()views.py:42sanitize: NONEsearch_products()models.py:89SQL INJECTION SINKCWE-89 · CRITICALDBSINKSOURCE⚠ VULNERABLE SINKRecommended FixProduct.objects.filter(name__icontains=query)ORM parameterization eliminates injection risk
Injection Sink Detection
SQL · NoSQL · LDAP Injection
OS Command Injection
Path Traversal
SSRF / CRLF / Log Injection
XSS (Reflected + Stored + DOM)
XXE / Template Injection
Inter-Procedural Taint
Cross-function data tracking
Module boundary tracing
Third-party API taint propagation
Alias and pointer analysis
Framework-aware (Django ORM, SQLAlchemy)
Custom sanitizer recognition
100%
Input traced
< 5%
FP Rate
17
Sink Types
We Secure Logic, Not Just Syntax

Business Logic & Authorization Security

Most scanners miss broken access control, IDOR, and privilege escalation — the most exploited vulnerabilities in production. We find what automated tools can't: flawed business intent.

Authorization Flaw Analysis — Real Patterns Found
CRITICALIDOR — Insecure Direct Object Referenceapi/orders.py:87
VULNERABLE
def get_order(order_id):
    return Order.objects.get(id=order_id)  # No ownership check!
FIXED
def get_order(order_id, user):
    return Order.objects.get(id=order_id, user=user)  # ✓ Fixed
Impact: Any authenticated user can view any other user's orders
CRITICALPrivilege Escalation — Role Assignment Bypassapi/users.py:234
VULNERABLE
def update_user(user_id, data):
    user.update(**data)  # Allows role: "admin" in payload!
FIXED
ALLOWED = ["name","email","phone"]  # whitelist
    user.update(**{k:v for k,v in data.items() if k in ALLOWED})
Impact: Regular user can promote themselves to admin via API
HIGHMissing Function-Level Authapi/admin.py:12
VULNERABLE
@app.route("/api/admin/export-users")
def export_users():  # No auth decorator!
FIXED
@login_required @admin_required
def export_users():  # ✓ Protected
Impact: Unauthenticated export of full user database
Auth Review Checklist
Object-level authorization (IDOR)
Function-level authorization
Field-level access control
Horizontal privilege escalation
Vertical privilege escalation
JWT/session validation
Rate limiting & brute-force
Password reset flow security
API key scoping
Admin bypass patterns
Multi-tenant data isolation
Workflow step enforcement
#1
OWASP Category
78%
Of Breaches (Auth)
100%
Manual Review
Zero
Auto-Miss Rate
We Don't Just Find Issues — We Fix Them

AI Auto-Fix & Guided Remediation

Context-aware AI generates secure code patches, opens fix PRs automatically, and provides developer-friendly explanations — cutting remediation time from days to minutes.

Spakto AI Auto-Fix · PR #2848 · SQL Injection — 3 filesAUTO-GENERATED
views.py · Lines 42-48
query = f"SELECT * FROM products WHERE name LIKE '%{search}%'"
results = db.execute(query)
+ results = Product.objects.filter(
+ name__icontains=search
+ ).select_related("category", "vendor")
password = hashlib.md5(password).hexdigest()
+ password = bcrypt.hashpw(password.encode(), bcrypt.gensalt())
AI Remediation Rationale
① Replaced string-interpolated SQL with Django ORM parameterized query (CWE-89 eliminated)
② MD5 password hashing replaced with bcrypt (work factor 12) — CWE-327 eliminated
③ Added select_related() to prevent N+1 query as a performance bonus
Auto-Fix Capabilities
Injection Fixes
SQL, XSS, SSRF, Command — parameterization + escaping
Crypto Upgrades
MD5/SHA1 → bcrypt/Argon2; AES-CBC → AES-GCM
Auth Hardening
Add @login_required, ownership checks, CSRF tokens
Secrets Rotation
Remove hardcoded creds, inject env var references
Dep Upgrades
Bump vulnerable packages, pin secure versions
Remediation Metrics
Auto-fixable vulnerabilities73% of all findings
Fix accuracy (no regression)96% validated by test suite
Dev time saved per finding89% vs manual remediation
Fix What Matters First — Context-Aware Risk

AI Risk Scoring & Smart Prioritization

CVSS alone isn't enough. Our AI weights exploitability, reachability, asset criticality, and business context to surface the 5% of findings that represent 95% of real risk.

Risk Dashboard · payment-service v2.4.1RISK SCORE: 8.7/10
SCR-001SQL Injection in payment processor9.8
CVSS 9.8EPSS .94Reach: HIGHPUBLIC PoC
Business: Payment data at risk
SCR-002IDOR on /api/orders/{id}8.8
CVSS 8.1EPSS .62Reach: HIGHTrivial
Business: All orders exposed
SCR-003JWT secret hardcoded in config.py7.9
CVSS 7.5EPSS .41Reach: MEDWith Access
Business: Auth bypass possible
SCR-004Dep: cryptography 41.0.3 CVE-2024-261306.1
CVSS 7.4EPSS .28Reach: LOWComplex
Business: DoS in edge case
SCR-005Missing CSRF on /admin/bulk-delete7.2
CVSS 6.8EPSS .18Reach: MEDSocial Eng.
Business: Bulk data loss
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Payment / PII / auth service? Higher weight.
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GitHub PR #2847 · Inline Security Annotations
src/auth/login.pyline 34CRITICAL
🤖
Timing Attack on Password Comparison

Using == for string comparison leaks timing information. Attackers can enumerate valid usernames by measuring response time differences.

Fix: Use hmac.compare_digest(a, b) for constant-time comparison
CWE-208CVSS 7.4Auto-fix available
src/api/upload.pyline 18HIGH
👤
Path Traversal in File Upload

filename parameter is not sanitized. Attacker can write to arbitrary paths using ../../../etc/cron.d/backdoor

Fix: Use os.path.basename(filename) and whitelist allowed extensions
CWE-22CVSS 8.1Auto-fix available
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