Daedalus wraps your infrastructure in thousands of constantly-rotating decoys — fake credentials, API keys, identities and endpoints that regenerate faster than they can be mapped. Agentic attackers waste time, compute and budget chasing ghosts, extract nothing, and trip a high-fidelity alert on every move.
Dynamic deception fabricPreemptive by designAgentless — no production impact
| ID | TYPE | SURFACE | ROTATES | LAST TOUCHED | STATUS |
|---|---|---|---|---|---|
| dcy_key_9f27c3 | AWS access key | github.com/acme/api | in 2h 41m | — | armed |
| dcy_s3_44aa10 | S3 bucket | prod-vpc | in 6h 02m | — | armed |
| dcy_oauth_b3e1f9 | OAuth app | Entra ID | held | 2m ago | tripped |
| dcy_db_71c4e0 | DB credential | vault | in 1h 58m | — | armed |
| dcy_adm_c507b2 | Admin console | prod-vpc | in 9h 26m | — | armed |
| dcy_svc_e83d17 | Service account | Entra ID | in 3h 33m | — | armed |
| dcy_sf_205b9d | Snowflake table | Snowflake | in 11h 07m | — | armed |
| dcy_key_4d19a8 | AWS access key | github.com/acme/web | in 0h 47m | — | armed |
Decoy fleet console — every asset above is synthetic.
The threat
AI didn’t just make attacks faster — it changed who’s attacking. Autonomous agents now run the full kill chain, gaining access after access while a human sleeps. These campaigns already happened.
AI-enabled attacks, year over year
CrowdStrike 2026
fastest recorded attacker breakout time
CrowdStrike 2026
secrets leaked in public code in 2025 (+34% YoY)
GitGuardian
new malicious open-source packages in 2025
Sonatype 2026
of breaches begin with stolen credentials — the #1 vector
Verizon 2025 DBIR
average time to find and contain a breach
IBM 2025
Sep–Nov 2025 · npm
A self-replicating worm tore through the npm ecosystem: it stole publish tokens, then republished itself into every package they could reach. 1,000+ packages hijacked, ~25,000 GitHub repos exposed — the first proof open-source malware can spread on its own.
StepSecurity · Wiz · Sonatype 2026
Nov 2025 · espionage
Anthropic caught an AI running a real espionage campaign. Claude executed 80–90% of it autonomously — recon, exploitation, credential theft, lateral movement — at thousands of requests per second across ~30 targets, with as few as 4–6 human decisions.
Anthropic, Nov 2025
2025 · offensive AI
An autonomous AI hacker climbed to #1 on HackerOne’s US leaderboard in about 90 days — submitting 1,060 confirmed vulnerabilities, 54 of them critical. Machine-scale offense stopped being hypothetical.
Dark Reading · XBOW, 2025
Attackers hand off stolen access in 22 seconds. The average breach still takes 241 days to find.
Daedalus closes that gap to the first interaction — a decoy is a confirmed intrusion the instant it’s touched, because nothing legitimate ever touches one.
Why it works
AI attackers are the first adversary with an exploitable cost structure — and a fatal habit: they check everything. A deception fabric that is massive and never holds still turns both against them.
The fabric regenerates and rotates continuously, so any map an attacker builds is stale before they use it. A static honeypot you find once and route around forever — a dynamic fabric you can never learn, because what’s real keeps moving.
An AI agent wins by being exhaustive — it enumerates everything, tirelessly, at machine speed. Across a fabric that’s mostly decoys and always shifting, checking everything means hitting traps. The harder it scales, the faster it trips.
Every request, verification and exfil attempt costs an agent tokens and compute. Old honeypots wasted a human’s afternoon; Daedalus drains a real budget line — until the attack’s unit economics go negative.
Nothing legitimate ever touches a decoy, so an interaction isn’t an anomaly to triage — it’s a confirmed intrusion, by definition. Zero false positives is a logical property, not a tuning job.
The economics
attacker cost
↑ 1,000×
time · compute · budget
Every probe hits a plausible target that has to be verified — and verification is the expensive part. Impose that cost a thousand times over.
your cost
flat
software-generated
Decoys are generated and rotated by software. Agentless, no production impact, no analysts babysitting traps.
attacker yield
$0
nothing worth taking
Real assets are indistinguishable from ten thousand synthetic ones. Nothing exfiltrated is worth anything.
| Budget spent | $50,000 |
|---|---|
| Compute burned | 41.6B tokens |
| Time lost | 23d 06h |
| Value extracted | $0.00 |
| Campaign ROI | −100.00% |
| Swarm status | TERMINATED |
How it works
Agentless. A distributed deception layer weaves thousands of decoys — fake credentials, keys, identities and endpoints — through your real infrastructure. No agents, no code changes, no production impact.
Every scan finds loot. Every piece of loot is synthetic, plausible and instrumented. From the outside, real and fake are indistinguishable — so the attacker has to check all of it.
Agentic attackers burn time, compute and budget verifying ghosts. Every decoy touch is a deterministic, high-fidelity alert — no legitimate user ever trips one, so there are zero false positives to triage.
Use cases
Everywhere a real asset lives and constantly regenerating — so wherever an agent looks, most of what it finds is fresh bait it has never seen before.
Decoy API keys, tokens and .env values across repos, pipelines and secret stores. Secret-scraping malware and AI code agents grab and validate them — the instant they test a fake, they’ve flagged themselves and wasted the harvest.
Fake privileged accounts, service accounts and OAuth apps in your IdP and directory. Credential stuffing, token abuse and lateral movement trip the first time one is used or enumerated.
Fake records, tables and documents woven into Salesforce, Snowflake and your data stores. An automated exfil sweep pulls a decoy before real data — silent bulk theft becomes a first-query alert.
Fake hosts, services, admin consoles, buckets and IAM roles beside the real ones — internal and internet-facing. Recon and east-west movement hit them, and public decoys burn an AI scanner’s budget before it reaches anything real.
Integrations
OktaMicrosoft EntraAWSSnowflakeGitHubCrowdStrikeSplunkWiz
Turns ‘assume breach’ from a principle into something you can see and act on — the detection layer Zero Trust leaves open.
High-fidelity, deterministic signal flows into the tools you already run. Less alert fatigue — not another console to watch.
An additive layer alongside your EDR, firewall and identity stack. Nothing to tear out to get value.
Every decoy interaction reveals real adversary TTPs against your environment — intelligence money can’t buy.
See the deception fabric running against a live agentic attack — deployed agentless, in your environment.