Your AI-generated code
is not secure.
Most AI-generated code ships without a single security control built in. Code:Sniper scans your source in minutes using AI agents backed by over a decade of penetration testing work. You upload, we scan, you get a report you can actually act on.
CRITICAL: Hardcoded AWS secret key
→ src/config.js:14 · AWS_SECRET_KEY = "AKIA…redacted"
CRITICAL: Second-order SQL injection
→ src/admin/reports.py:88 · stored user.bio reflected into admin report query
HIGH: Insecure deserialization (pickle)
→ src/middleware/session.py:31 · pickle.loads() on signed session cookie payload
HIGH: Session pollution, cross-tenant key collision
→ src/auth/session_store.py:57 · unscoped Redis key auth:{user_id}
HIGH: Server-side prototype pollution
→ src/utils/deepMerge.ts:22 · __proto__ reachable via request body
MEDIUM: JWT HS256 with default fallback secret
→ src/auth/jwt.py:14 · os.getenv("JWT_SECRET", "dev-secret")
MEDIUM: Outdated dependency with known CVE
→ lodash@4.17.20 → CVE-2021-23337
Report generated. 2 critical, 6 high, 9 medium findings.
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2. AI Analyzes
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10+
Years pentesting experience
20+
General security tools
35+
Specialized analysis modules
44+
Programming languages
~10 min
Average scan time
100%
EU Data Residency
Zero
Data Retention
AA
WCAG 2.1 Standard
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Two tiers for any team size, plus one optional human review add-on. Cancel anytime.
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One account for one developer or security engineer at a registered company. Unlimited ZIP scans and every specialist agent, billed per seat.
- Unlimited code scans
- ZIP file upload support
- Code paste scans (up to 1,000 lines)
- 20+ general security tools
- 35+ specialized analysis modules
- Specialist AI agents for OWASP, Logic, Crypto & more
- Downloadable Markdown reports
- LLM-compatible remediation files
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Multiple seats, shared projects, priced per company.
- Everything in Pro for your whole team, plus:
- Multi-seat licence with central billing
- Custom admin interface
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- Audit trail with CSV export
- Company-wide project visibility
- Prioritized support
- Human-review SLAs tailored to you
- Higher usage limits
- Dedicated onboarding & security review
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A senior penetration tester with OSCP+ and OSED certifications from OffSec personally reviews your results, weeds out false positives, and gives you remediation guidance grounded in real engagement experience. Bookable per project on any tier, Pro or Enterprise.
- Manual review by certified pentester
- False positive elimination
- Business context-aware recommendations
- Priority support channel
- Available in fluent English and German
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Code:Sniper is a professional offensive-security tool, and we take that seriously. Before activating a paid tier we verify legal company information such as a commercial register entry or equivalent. If you are a legitimate security researcher without a company entity, reach out(opens in new tab) and we will review your case personally.
Why not just use a model and a prompt?
A model answers a prompt. Code:Sniper runs a complete security engagement around the model - here is what that actually means.
Code:Sniper Full security engagement | A single prompt One model, your own instructions | A frontier model alone The latest single model, on its own | A general AI assistant Chat / coding copilot | |
|---|---|---|---|---|
| Runs 20+ deterministic security tools (SAST, secret & dependency scanners) | ||||
| 35+ specialized analysis modules, each with its own threat model | ||||
| Prompts & audit rules hand-written by certified pentesters | ||||
| Guaranteed whole-repository coverage, independent of the context window | ||||
| Findings grounded in verified tool evidence, not guesswork | ||||
| Independent cross-agent validation & false-positive filtering | ||||
| Reproducible, structured report + ready-to-apply AI remediation file | ||||
| Gets stronger as the underlying models improve (model-agnostic) | ||||
| Optional review by a certified human pentester |
Frequently asked questions
The honest, technical answers to what teams ask us most.
Because a prompt gives you a single, unverified opinion - Code:Sniper runs a full security process around the model. Four things a raw prompt cannot do:
- Coverage. A prompt is bounded by the context window. A real codebase does not fit, so the model silently skips files. Code:Sniper greps every relevant sink across the entire tree and forces a verdict on each one - coverage does not depend on what fits in a prompt.
- Grounding. A bare model guesses. Code:Sniper first runs 20+ deterministic tools and feeds the verified evidence to its specialists, so findings are anchored in real tool output, not vibes.
- Specialization. One prompt is one generalist pass. Code:Sniper runs 35+ specialized modules in parallel - OWASP, cryptography, business logic, secrets, per-language specialists - each with its own threat model.
- Validation. The model that finds an issue is also the one that "confirms" it - there is no independent check. Code:Sniper adds a cross-agent validator and a fact-checker that flips false positives and de-duplicates before anything reaches your report.
On top of that you get a reproducible, structured report: the same code yields the same result every run, instead of different free-form prose each time.
The opposite - a better model makes Code:Sniper better, not redundant. Code:Sniper is an orchestration layer, not a model.
We already run multiple models under the hood with automatic fallback and a deep-reasoning mode for hard cross-file analysis. When a stronger model ships, we plug it in and every customer benefits the same day - without losing the parts a raw model will never give you on its own:
- the 20+ deterministic tools that ground every finding,
- the pentester-authored prompts and curated audit rules,
- the independent cross-agent validation and false-positive filtering,
- the guaranteed whole-repository coverage,
- and the reproducible, structured report plus AI-ready remediation file.
A model answers a prompt. Code:Sniper runs a complete security engagement around that model - and rides the same improvement curve while keeping everything else.
No. The LLM is one component inside a pipeline. Before any model is invoked, your code is validated, safely extracted and scanned by 20+ deterministic security tools. Those verified results are routed to specialist agents driven by prompts and curated Semgrep rules we wrote ourselves.
After the agents run, a cross-agent validator reconciles conflicts, a fact-checker removes false positives, duplicates are merged deterministically, and the final report is rendered directly from structured data - not re-generated by a model. Take the LLM out and you still have a working scanner; take the orchestration out and a model alone cannot reproduce any of it.
A coding assistant reasons over the few files in its context and optimizes for writing code, not for adversarial review. It has no deterministic scanners, no guaranteed coverage of your whole repository, no independent validation, and no reproducible output.
Code:Sniper is built by offensive-security specialists to think like an attacker: it sweeps every sink in the codebase, grounds findings in tool evidence, runs domain specialists in parallel and hands you an actionable, structured report - plus an optional review by a certified human pentester.
That is exactly why we never rely on the model alone. Real bugs are caught deterministically: every specialist greps its language-specific sinks across the full tree and must return a verdict on each match, so coverage is not left to the model's discretion.
Invented bugs are filtered out: a dedicated fact-checking stage cross-references each agent finding against the actual source and tool output, flips false positives, and de-duplicates before the report is written. Curated audit rules encode known issues permanently, with zero inference involved.
Code:Sniper includes those tools and goes further. Classic SAST is great at pattern matches but drowns you in noise and cannot reason about business logic, multi-step exploit chains, or context-dependent flaws.
Code:Sniper runs the scanners for you, then layers specialist agents on top to interpret the results, find logic and cross-file issues that pattern matching misses, strip false positives, and deliver one prioritized report with concrete remediation - instead of several raw tool outputs you have to triage by hand.