Attack Surface - AI Applications | 0din.ai

Attack surface • AI Applications
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.
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.
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.
RAG poisoning · Document-borne injection · API-response injection · Upload-based payloads
System prompts, retrieved source documents, other-tenant data, or internal state surfacing into responses, logs, or analytics events your application emits.
System-prompt extraction · RAG source exfiltration · Cross-tenant leakage · PII spill into logs
Model output flowing into code execution, database queries, email, function calls, or generated UI: attacker-shaped strings reaching systems that trust them.
SQL/code injection via output · Markdown/HTML payloads · Unsafe tool-arg generation · Malicious link rendering
Give us one AI application your team needs to approve. We'll run a focused first-pass assessment and show where controls hold, where they break, and what to do next.
Backed by Mozilla •Researcher-led
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.
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Testing, intelligence, and in-app controls mapped to AppSec, red-team, and AI-platform work, from pre-launch exploit testing to production input scoring.
Defensible evidence and framework-mapped findings, so AI risk maps to the controls your auditors and regulators already expect.
Real-time input scoring, abuse-pattern coverage, and unsafe-output testing, to keep user-facing AI safe at scale.
Test, score, and match against real exploits, covering guardrail bypass, data exposure, and unsafe output on conversational surfaces.
Coverage for the multi-input failure modes unique to LLM apps: indirect injection, cross-context leakage, and downstream exploitation.
Securing autonomous agents, from goal-hijack and tool-abuse testing to per-step input scoring and action gating.
Everything you need before you ship: adversarial testing, red-team probes, and a local-first SDK that drops into your stack.
Local classification and signature matching backed by researcher-validated exploit coverage: detection that runs in your environment.
Act on risk in real time: gate, route, tune, and triage using each input’s score and match severity.
Audit-ready evidence: framework-mapped findings and reproducible tests your stakeholders can trust.
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.
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.
AI Vulnerability Intelligence
Packages researcher-validated probes to extend your internal red-team, integrated into standard tooling.
AI Vulnerability Intelligence
Matches inputs against known control-failure and untrusted-input patterns, with severity metadata on every hit.
Scores each prompt on a 0–1 suspicion scale at the application boundary, in-process.
Tags every finding to recognized AI-security frameworks, so results are defensible without manual re-mapping.
AI Vulnerability Intelligence
Draws on real-world exploits discovered by 0DIN bug-bounty community and validated by our researchers.
Tags every finding to recognized AI-security frameworks, so results are defensible without manual re-mapping.
Traces each finding to a reproducible test, producing defensible reports for auditors and stakeholders.
AI Vulnerability Intelligence
Extends framework mapping toward broader AI-governance regimes.
AI Vulnerability Intelligence
Draws on real-world exploits discovered by 0DIN bug-bounty community and validated by our researchers.
Ability to match severity to gate, log, or route requests and actions.