SECURITY & LOGIC AUDIT

Automating the
Expert Eye.

Evaluating LLMs on their ability to detect subtle logic bugs, CWE vulnerabilities, and performance regressions. We measure the critical balance between recall (catching the bug) and precision (avoiding false positives).

View Benchmarks
src/auth/jwt_handler.py 1 vulnerability found
41
def verify_token(token: str, secret: str):
42
try:
43
payload = jwt.decode(token, options={"verify_signature": False})
43
payload = jwt.decode(token, secret, algorithms=["HS256"])
Acadify_Security_Agent detected CWE-347
CRITICAL: Improper Verification of Cryptographic Signature. Disabling signature verification allows attackers to trivially forge administrative tokens. I recommend enforcing the HS256 algorithm and requiring the secret key for decoding.
44
return payload
45
except jwt.ExpiredSignatureError:

Precision Evaluation

A code reviewer that flags every line is useless. We benchmark LLMs heavily on their False Positive Rate (FPR) to ensure they are helpful tools, not noisy distractions.

10k+

Expert-Annotated Bugs

A curated dataset of subtle race conditions, off-by-one errors, and memory leaks sourced from real-world production outages.

< 5%

Target FPR Penalty

Models are heavily penalized in our scoring system for 'hallucinating' bugs or providing overly pedantic stylistic nitpicks.

CWE/CVE

Security Taxonomy

Evaluations mapped directly to the Common Weakness Enumeration framework, testing zero-day recognition capabilities.

Beyond
Static Analysis.

Traditional linters catch syntax. We train and evaluate LLMs to catch semantic and architectural flaws that require deep understanding of the developer's intent.

Evaluation Dimensions

Cross-File Dependency Logic

Can the model detect when a change in `api_router.py` breaks an assumption made in `database_schema.sql`?

Asymptotic Complexity (Big-O)

Identifying N+1 query problems or unintended nested loops that will cause performance regressions under load.

Security Posture

Catching hard-coded secrets, SQL injection vectors, and improper input sanitization before they hit staging.

Code Review FAQ

Understanding our automated security audit and PR review benchmarks.

Static analysis tools rely on predefined rules. They are excellent for catching anti-patterns but cannot understand business logic. LLMs excel at finding "logic bugs"—where the code compiles perfectly but implements the wrong intent.

We feed the model perfectly valid, secure, and highly-optimized code snippets. If the model flags an issue or suggests a "fix" that either breaks the code or provides no tangible benefit, it receives a severe penalty to its precision score.