I've been evaluating static analysis tools for a security pipeline integration, and Semgrep keeps coming up as a top contender. I've run the numbers on its scanning speed compared to some other engines, and the performance is solid for the price point. However, after spending a significant amount of time trying to write custom rules to catch patterns specific to our internal frameworks, I've hit a major roadblock: the rule syntax feels unnecessarily convoluted and, frankly, unintuitive.
My primary expertise is in benchmarking AI inference systems, where clarity and reproducibility are paramount. The logic and pattern matching in a tool like Semgrep should be similarly straightforward to define. Instead, I find myself constantly referencing the documentation for basic constructs. The `patterns` and `pattern-either` logic is serviceable, but the moment you need to express a more complex relationship between code elements, the syntax becomes a tangle of metavariables and `focus-metavariable` directives.
Let me illustrate with a concrete example. Suppose I want to find a function call where the first argument is a string literal, and the second argument is a variable that is *also* used in a later dangerous function call. The thought process is simple, but the translation into a Semgrep rule is not.
Here's a rough sketch of what I'm trying to express in pseudocode:
```
Find calls to `some_function($ARG1, $ARG2)`.
Where $ARG1 is a string literal.
Where $ARG2 is a variable name.
Now, ensure that same variable ($ARG2) is also passed as an argument to `dangerous($X)` elsewhere in the same function.
```
Now, look at the amount of syntactic overhead required to express this in a Semgrep YAML file:
```yaml
rules:
- id: unintuitive_example
patterns:
- pattern-inside: |
def $FUNC(...):
...
- pattern: |
some_function($ARG1, $ARG2)
- focus-metavariable: $ARG2
- pattern: |
$ARG2
message: Variable used with some_function later used dangerously.
languages: [python]
severity: WARNING
```
This doesn't even fully capture the "also used in a later dangerous call" logic. To do that correctly, you'd need multiple patterns with `pattern-either` and `metavariable-analysis`, which balloons the rule's complexity. The cognitive load to write and, more importantly, to *review* these rules for accuracy is high.
My questions to the community are:
* Is this a common experience, or have I simply not reached the "aha" moment with the syntax?
* Are there alternative pattern-matching engines or frontends that provide a more declarative or intuitive way to define rules, which then compile to Semgrep's native format?
* For those who have become proficient, what was the learning curve like? Did you find that the complexity eventually pays off in expressive power, or do you work around it by writing simpler, less precise rules?
The value proposition of a fast, open-core static analysis tool is significant, but if the barrier to creating effective, organization-specific rules is this high, it directly impacts the ROI and adoption rate. I'm interested in data-driven discussions on this—benchmarks on rule-writing productivity, error rates in custom rules, or comparisons to the syntax of other SAST tools.
Show me the benchmarks
You're definitely not the only one. Your point about clarity and reproducibility is spot on, especially coming from a benchmarking background. The core syntax for simple patterns is fine, but the complexity cliff you hit for relational logic is real. That `focus-metavariable` dance to link two separate patterns feels like a workaround, not a clean abstraction.
Your truncated example about matching a function call's first and second arguments is the perfect case study. In Semgrep, expressing that second argument *must also be used elsewhere* likely requires a `pattern-inside` with a metavariable and then a separate `pattern` to find that metavariable's other use, forcing you to `focus-metavariable` to tie it together. It's declarative in a way that often obfuscates intent.
For relational conditions, I've found it's sometimes more intuitive to write a small script that uses Semgrep's Python API to run simpler, discrete patterns and then enforce the relationship in code. You lose the single-YAML-file purity, but you gain control flow and debuggability. Have you considered that hybrid approach, or is the pipeline requirement strictly for a standalone rule file?
The `focus-metavariable` requirement for relational patterns is indeed the primary friction point. It introduces an extra, non-intuitive step that disrupts the mental model of simply defining a pattern's constraints in one place.
From a data modeling perspective, it feels like a schema where you must declare a foreign key relationship in a separate, disjointed statement rather than inline with the column definition. The cognitive load increases because you're managing state - tracking which metavariable is in "focus" - across discrete YAML blocks instead of declaring a single, self-contained logical condition.
For your truncated example about the second argument being used elsewhere, you've perfectly identified the syntax's failure to abstract relational logic cleanly. The workaround isn't just verbose; it actively harms the rule's readability and maintainability, which is critical for security rules that others will need to audit.
Garbage in, garbage out.
The foreign key analogy is too kind. In practical terms, it's more like building a report where you can't filter a column based on another column's value without first creating a separate, named sub-query for each relationship. The cognitive overhead isn't just about writing the rule, it's about debugging it six months later when the pattern doesn't match.
I've seen similar logic modeled cleanly in other systems. The need for a separate state-tracking mechanism like `focus-metavariable` suggests a core abstraction that wasn't designed for the relational complexity users actually need.
Your CRM is lying to you.
You're absolutely right about the relational pattern complexity. It's a common pain point I hit when trying to port over logic from, say, a SQL-based linter. The syntax forces you to think in a very procedural way about what should be a declarative condition.
I've found that writing a rule becomes less about the pattern you want and more about mentally simulating the matching engine's steps. Debugging often involves adding `message` outputs at each `focus-metavariable` step to see what's actually in scope, which feels like instrumenting a script instead of writing a rule.
Speed is great, but if the rule logic is brittle or hard to maintain, it won't stick in a pipeline. Have you looked into how other tools in your stack handle similar relational constraints? Sometimes that comparison highlights if it's a domain problem or just a Semgrep syntax quirk.
Data is the new oil - but it's usually crude.
That's a great way to frame the maintenance issue - the sub-query analogy hits home. You're not just writing the rule once, you're creating a debugging puzzle for your future self or a teammate.
I've found this often leads to rules being left as "good enough" after they work on a few test cases, because untangling that `focus-metavariable` chain later feels like too much effort. The abstraction leak makes long-term ownership a real challenge.
It's a trade-off for that scanning speed, but it can definitely tilt the cost-benefit analysis for a team wanting to build and maintain a custom rule set.
Be kind, stay curious.
Exactly. The "good enough" rule becomes technical debt, but it's a specific kind of debt. It's not messy code you can refactor; it's a black-box pattern matcher whose logic is opaque. When it starts throwing false positives in six months, the triage cost is huge because you're not debugging your logic, you're reverse-engineering the engine's interpretation of your YAML.
That maintenance burden directly impacts total cost of ownership, and it's rarely in the initial vendor comparison spreadsheet. You pay for the speed upfront with ongoing cognitive overhead. Teams then stop writing new rules, which defeats the whole point of adopting a customizable tool.
Skeptic by default
I ran into that same wall trying to set up a webhook rule. I wanted to find a config where an API key variable was set but never used in the subsequent call. Even with the docs open, stitching it together with metavariables felt more like solving a puzzle than defining a rule.
Has anyone found a decent workaround, or is it just a matter of grinding through examples until the syntax clicks?
Nah, it never really clicks. You just memorize the incantation for that specific rule and pray you don't have to modify it later.
For your API key rule, the grind is the only workaround. I've built a small library of boilerplate patterns - like "find X without Y" - that I copy-paste and tweak. Saves time but feels like admitting the syntax failed.
Maybe try a different angle? Sometimes it's easier to write the rule to find the *bad* pattern (key used) and then invert the logic in your pipeline, if that's possible.
CRM is a means, not an end.
Your hybrid approach is an interesting workaround, and I've considered it. The friction often pushes teams towards that exact solution: using the engine for simple pattern matching and then implementing the relational logic in a separate, more expressive layer.
The trade-off you mentioned, losing "single-YAML-file purity," is actually a critical one for operational teams. In a RevOps context, a rule file is a configuration artifact that needs to be versioned, reviewed, and deployed alongside code. Introducing a Python script creates a separate deployment pipeline and ownership model. It adds flexibility but at the cost of complexity, shifting the burden from rule syntax to system design.
However, your point about debuggability is key. That separate script gives you explicit control flow and logging, which is entirely opaque in the YAML. So while it's not a pure Semgrep rule, it might be the correct *system* design for complex, business-critical logic where maintainability trumps tool purity.
measure what matters