Cache Poisoning

Attackers exploit the semantic matching logic of modern caches by crafting queries that are semantically adjacent to legitimate requests but carry malicious payloads. In sovereign inference caching, an adversary might submit a prompt whose embedding falls within the similarity threshold of a trusted query, causing the cache to associate a compromised LLM response with a high-traffic intent. This bypasses exact-key matching defenses and poisons the embedding space itself.

The attacker intercepts or manipulates the response from the origin model before it is written into the cache. Common techniques include:

• Man-in-the-Middle (MITM) interception between the cache and inference endpoint
• DNS spoofing to redirect cache miss requests to a rogue model server
• API response tampering via compromised upstream dependencies Once the tainted response is cached, every subsequent cache hit serves the poisoned payload to unsuspecting users, amplifying the attack's blast radius.

Adversaries deliberately engineer hash collisions or predict cache key generation patterns to overwrite legitimate entries. In sovereign environments using Locality-Sensitive Hashing (LSH) for semantic caching, an attacker can generate adversarial examples—inputs specifically designed to produce the same hash bucket as a high-value target query. This causes the cache to evict or overwrite the genuine response with attacker-controlled content, effectively hijacking the cache slot.

Cache poisoning often exploits predictable Time-To-Live (TTL) expiration patterns. Attackers monitor cache refresh cycles and time their injection to coincide with the moment a popular entry expires and the cache fetches a fresh copy. During this cache stampede vulnerability window, the adversary floods the origin with poisoned responses, hoping one gets cached before the legitimate response arrives. This race condition is particularly dangerous in high-concurrency sovereign inference deployments.

In multi-tenant sovereign caching layers with insufficient tenant isolation, an attacker in one tenant context can poison cache entries that are served to other tenants. This occurs when:

• Cache keys lack proper namespace segmentation
• Embedding spaces are shared across organizational boundaries
• Eviction policies don't respect tenant-specific partitioning The result is lateral contamination where a compromised tenant poisons the inference outputs of unrelated, trusted users sharing the same cache infrastructure.

The primary mitigation is cryptographic signing of cache entries. Each response is hashed and signed by the trusted origin model using a hardware-backed root of trust. Before serving a cached response, the cache layer verifies the signature against a known public key. Any tampering invalidates the signature, triggering an automatic eviction and re-fetch. In sovereign deployments, this is combined with encrypted caches and immutable audit logs to provide non-repudiation of every served inference result.

The cryptographic protection of data at rest within the caching layer. In sovereign environments, AES-256-GCM is standard for encrypting stored embeddings and response payloads. This ensures that even if an attacker gains physical access to the storage medium, cached inference results remain confidential. Key management must integrate with on-premises Hardware Security Modules (HSMs) to prevent key exfiltration. Without encryption, a poisoned cache entry containing malicious code or exfiltrated data is trivially readable by any compromised internal service.

An immutable, cryptographically signed storage system for model artifacts and cache validation data. Every cached response should be paired with a content hash stored in a tamper-proof registry. Before serving a cached result, the system verifies the hash against the registry. If a cache poisoning attack modifies a stored response, the hash mismatch immediately triggers an alert and blocks the compromised entry. This provides cryptographic proof of integrity for every item in the semantic cache.

The practice of storing responses indicating a 'not found' or error state. An attacker can exploit this by flooding the cache with requests for non-existent keys, causing the system to repeatedly query the origin model. Negative caching stores these empty results for a short TTL, absorbing the attack. However, a sophisticated poisoning attack might inject a negative cache entry for a legitimate key, causing a denial of service. Proper input validation on what gets negatively cached is a critical defense.

A multi-tenancy security control that logically or physically separates cached data. In a sovereign deployment serving multiple departments, a cache poisoning attack in one tenant's partition must never contaminate another's. Techniques include:

• Namespace partitioning at the key level
• Hardware-enforced enclaves per tenant
• Strict access control lists on cache read/write paths Without isolation, a single compromised client can poison the shared cache for all users.

A stability pattern that automatically stops serving requests to a failing backend. If a cache poisoning attack causes the origin model to return corrupted data, the circuit breaker detects the anomaly (e.g., via response validation) and opens the circuit, immediately failing fast. This prevents the poisoned data from being written into the cache and served to other users. The system can then fall back to a known-good static response or a previous cache snapshot.

The automated collection of metrics, traces, and logs for observability. Detecting a cache poisoning attack requires monitoring:

• Sudden drops in semantic similarity between cached responses and expected outputs
• Anomalous write patterns to the cache
• Unexpected changes in response entropy or token distribution Real-time telemetry dashboards allow security operations teams to identify and quarantine poisoned entries before they are widely served.