Attack surface • AI Applications
Ship AI features your auditor can defend.
Copilots, RAG assistants, in-product LLM features. We run 20,000+ researcher-validated probes against the AI-integrated parts of your application, every finding tied to a named researcher, a disclosure date, and a bounty on record. Maps to OWASP LLM Top 10 and MITRE ATLAS, built for AppSec, red-team, and AI-platform teams.
Your AI Feature Has Three Failure Modes. 0DIN Finds Them First.
Every AI feature you embed has the same three failure modes, and they don't show up in synthetic benchmarks. We test against the long tail of researcher-validated exploits that production traffic eventually finds anyway.
01
Indirect injection
Adversarial instructions arriving as data: through retrieved documents, file uploads, API responses, or tool outputs. The model treats them as commands; the user never typed.
What we find
RAG poisoning · Document-borne injection · API-response injection · Upload-based payloads
02
Cross-context leakage
System prompts, retrieved source documents, other-tenant data, or internal state surfacing into responses, logs, or analytics events your application emits.
What we find
System-prompt extraction · RAG source exfiltration · Cross-tenant leakage · PII spill into logs
03
Downstream exploitation
Model output flowing into code execution, database queries, email, function calls, or generated UI: attacker-shaped strings reaching systems that trust them.
What we find
SQL/code injection via output · Markdown/HTML payloads · Unsafe tool-arg generation · Malicious link rendering
0DIN CAPABILITIES
See 0DIN from where you sit.
0DIN secures generative AI across its whole lifecycle: testing, detection, exploit intelligence, and audit-ready reporting. Pick your lens and we’ll surface the capabilities that matter most to you.
Show me solutions for
Coverage for the multi-input failure modes unique to LLM apps: indirect injection, cross-context leakage, and downstream exploitation.
Defends against:
Indirect injection Cross-context leakage Downstream exploitationScanner
Indirect-injection testing
Runs curated 0DIN and community garak probes against API and web-chat AI targets to surface jailbreaks, policy bypasses, and safety-control failures before they reach users.
Prompt Toolkit / SDK
Boundary input scoring
Scores each prompt on a 0–1 suspicion scale at the application boundary, in-process.
Prompt Toolkit / SDK
Downstream-exploit guarding
Ability to match severity to gate, log, or route requests and actions.
AI Vulnerability Intelligence
Retrieval-poisoning coverage
Matches inputs against known control-failure and untrusted-input patterns, with severity metadata on every hit.
AI Vulnerability Intelligence
Synced exploit feed
New attack techniques sync automatically as researchers discover them.
Scanner
Pre-launch & regression
Schedules recurring or triggered re-scans across the same targets and probe sets based on updates to catch regressions after model, prompt, policy, or app changes.
Tailored for your Team
Researcher-validated AI intelligence security packages
Scanner
Turnkey AI security testing.
Probe library, dashboards, scheduled scans, custom probe import, PDF reports, SIEM export.
Best fit for
Large CISO orgs and regulated enterprises running structured red-team programs.
AI Vulnerability Intelligence
The data your red team's been building from scratch.
Curated, versioned Probe Packs + intelligence feed. JSONL/YAML for PyRIT, Garak, or your own scanner.
Best fit for
Teams already running their own tooling who want a curated, researcher-validated probe feed.
Prompt Toolkit / SDK
Detection your platform can ship.
Embedded detection SDK for prompt-based attacks and agent threat hunting.
Best fit for
AppSec teams or platform vendors who need detection signals inline with their existing security tools.
Use Case Matrix
How different teams use 0DIN to secure their AI applications.
Security Consultants
AppSec + Red Team
Pre-launch testing
Stress-test new AI features before customer rollout. Catch indirect-injection paths before users do.
Continuous validation
Scheduled re-scans on every model swap, prompt change, or RAG-index update. Find regressions in your retrieval pipeline.
Red-team augmentation
Researcher-validated probes augment your internal red-team library. JSONL drops into PyRIT or Garak.
Legal & Compliance
GC + Privacy + Audit
Audit-ready reports
Every AI application finding tagged to OWASP LLM Top 10 + MITRE ATLAS. Defensible without re-mapping.
Data retention discipline
Prompts and responses captured during testing are deleted after report delivery. Vendor disclosure path on upstream findings.
Control narrative inputs
Test results become evidence your control narrative can reference. Same vocabulary your auditor already uses.
Trust & Safety
Policy + Brand + Content
Brand-safety validation
Test against your defined safe-content policy. Document where the AI feature violates your stated rules.
Regulated-domain testing
Healthcare, financial, legal probes against your AI feature before it goes live in a regulated context.
Policy gap detection
Find the gap between what your AI-use policy says and what your AI feature actually does.
Independent. Researcher-led. Mozilla-backed.
25+ yrs
Building trust on the open internet. Built on the same trust, transparency, and commitment to a safer internet that's defined Mozilla.
2,100+
Real researchers actively probing AI systems. We convene a global community of security experts.
20K+
Human-authenticated probes across industries.