pith:E4LQU2RR
Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
Neural networks can hide backdoors as statistically indistinguishable latent directions, reducing detection to an intractable hypothesis test on model parameters.
arxiv:2605.13214 v1 · 2026-05-13 · cs.CR · cs.LG
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Record completeness
Claims
if exploitable channels within a network's latent space are statistically indistinguishable from naturally learned directions, an attacker need not introduce foreign structure but can instead exploit the geometry the network already possesses.
The hypothesis test between clean and backdoored parameter distributions is intractable in practice for state-of-the-art models; this is stated as a conjecture without a formal reduction or hardness proof.
Backdoors can be realized as statistically natural latent directions in modern neural networks, achieving high attack success with negligible clean accuracy loss and resisting existing defenses.
References
Receipt and verification
| First computed | 2026-05-18T03:08:48.498585Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
27170a6a3105e8a8e3577b5fc8d95854151c879d172e04ba07e0a4ed0b917ed2
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/E4LQU2RRAXUKRY2XPNP4RWKYKQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 27170a6a3105e8a8e3577b5fc8d95854151c879d172e04ba07e0a4ed0b917ed2
Canonical record JSON
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